This documents an MDD TWAS study. The analysis was carried out by Lorenza Dall’Aglio and Oliver Pain, with supervision from Cathryn Lewis.

This study used GWAS summary statistics from the Wray et al Major Depression GWAS, including 23andMe participants. We used SNP-weights derived by FUSION for brain tissues, HPA tissues, HPT tissues, and blood.

After reviewer comments, we also included SNP-weights derived by the PsychENCODE team for the DLPFC. The PsychENCODE SNP-weights were derived using all HRC imputed variants within the PsychENCODE dataset. The standard FUSION LD reference is restricted to HapMap3 variants, so when using the PsychENCODE SNP-weight we used an unrestricted version of the 1KG Phase 3 reference to improve SNP overlap.


1 Estimating transcriptome-wide significance threshold

First we need to estimate our transcriptome-wide significance threshold to account for the number of features tested. We will use a permutation-based approach to determine the appropriate transcriptome-wide significance threshold. It works by performing TWAS using the relevent SNP-weight panels.

Show a list of SNP-weight panels in the TWAS

SNP-weight panels used in MDD TWAS
Panel
Adrenal_Gland
Brain_Amygdala
Brain_Anterior_cingulate_cortex_BA24
Brain_Caudate_basal_ganglia
Brain_Cerebellar_Hemisphere
Brain_Cerebellum
Brain_Cortex
Brain_Frontal_Cortex_BA9
Brain_Hippocampus
Brain_Hypothalamus
Brain_Nucleus_accumbens_basal_ganglia
Brain_Putamen_basal_ganglia
Brain_Substantia_nigra
CMC.BRAIN.RNASEQ
CMC.BRAIN.RNASEQ_SPLICING
NTR.BLOOD.RNAARR
Pituitary
Thyroid
Whole_Blood
YFS.BLOOD.RNAARR
PsychENCODE

Generate null distribution

mkdir -p /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig

for batch in $(seq 1 20); do
sbatch -p brc,shared -n 1 --mem=10G /users/k1806347/brc_scratch/Software/Rscript.sh /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/TWASPermuThr.R \
--nperm 50 \
--ncore 1 \
--weights /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/Batch${batch}
done

Estimate transcriptome-wide significance threshold

library(data.table)
library(MKmisc)

# Create list of files containing minimum p values
batches<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/',pattern='Batch*')

# Combine all the minimum p-values
min_P_all<-NULL
for(batch in batches){      
    min_P_all<-c(min_P_all,fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/',batch), header=F)$V1)
}

# Calculate the 5th percentile of the minimum p-values
TWalpha<-MKmisc::quantileCI(x=min_P_all, prob=0.05, method="exact",conf.level=0.95)
TWalpha # 1.368572e-06

# Calculate the 0.1th percentile which will be needed for the high-confidence associations section
TWalpha_001<-MKmisc::quantileCI(x=min_P_all, prob=0.001, method="exact",conf.level=0.99)
TWalpha_001 # 3.685926e-08

# Save the R object for future reference
saveRDS(TWalpha,file='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/TWASsign_05.RDS')
saveRDS(TWalpha_001,file='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/EstTWSig/TWASsign_001.RDS')

2 TWAS


2.1 FUSION SNP-weights


2.1.1 GWAS summary statistics preparation

GWAS summary statistics were munged using the LDSC munge_sumstats.py.

Show munge log file

## *********************************************************************
## * LD Score Regression (LDSC)
## * Version 1.0.0
## * (C) 2014-2015 Brendan Bulik-Sullivan and Hilary Finucane
## * Broad Institute of MIT and Harvard / MIT Department of Mathematics
## * GNU General Public License v3
## *********************************************************************
## Call: 
## ./munge_sumstats.py \
## --N-con-col Ncon \
## --out /mnt/lustre/groups/ukbiobank/sumstats/munged/DEPR01 \
## --merge-alleles /mnt/lustre/groups/ukbiobank/Edinburgh_Data/usr/helenaG/ldsc-master/w_hm3.snplist \
## --N-cas-col Ncas \
## --N-col N \
## --info-min 0.6 \
## --sumstats /mnt/lustre/groups/ukbiobank/sumstats/cleaned/DEPR01.gz 
## 
## Interpreting column names as follows:
## INFO:    INFO score (imputation quality; higher --> better imputation)
## SNP: Variant ID (e.g., rs number)
## N:   Sample size
## A1:  Allele 1, interpreted as ref allele for signed sumstat.
## P:   p-Value
## A2:  Allele 2, interpreted as non-ref allele for signed sumstat.
## Ncon:    Number of controls
## Ncas:    Number of cases
## FREQ:    Allele frequency
## OR:  Odds ratio (1 --> no effect; above 1 --> A1 is risk increasing)
## 
## Reading list of SNPs for allele merge from /mnt/lustre/groups/ukbiobank/Edinburgh_Data/usr/helenaG/ldsc-master/w_hm3.snplist
## Read 1217311 SNPs for allele merge.
## Reading sumstats from /mnt/lustre/groups/ukbiobank/sumstats/cleaned/DEPR01.gz into memory 5000000.0 SNPs at a time.
## Read 10155339 SNPs from --sumstats file.
## Removed 8953119 SNPs not in --merge-alleles.
## Removed 0 SNPs with missing values.
## Removed 0 SNPs with INFO <= 0.6.
## Removed 17182 SNPs with MAF <= 0.01.
## Removed 0 SNPs with out-of-bounds p-values.
## Removed 0 variants that were not SNPs or were strand-ambiguous.
## 1185038 SNPs remain.
## Removed 0 SNPs with duplicated rs numbers (1185038 SNPs remain).
## Removed 0 SNPs with N < 307422.666667 (1185038 SNPs remain).
## Median value of OR was 1.0, which seems sensible.
## Removed 0 SNPs whose alleles did not match --merge-alleles (1185038 SNPs remain).
## Writing summary statistics for 1217311 SNPs (1185038 with nonmissing beta) to /mnt/lustre/groups/ukbiobank/sumstats/munged/DEPR01.sumstats.gz.
## 
## Metadata:
## Mean chi^2 = 1.534
## Lambda GC = 1.419
## Max chi^2 = 79.045
## 597 Genome-wide significant SNPs (some may have been removed by filtering).
## 
## Conversion finished at Wed Feb 14 12:45:03 2018
## Total time elapsed: 2.0m:19.39s

Then, I modified the sumstats to remove the rows with missing values.

Show code

module add general/R/3.5.0
R

library(data.table)

# Read in the LDSC munged sumstats using zcat to unzip the file
sumstats<-data.frame(fread('zcat /mnt/lustre/groups/ukbiobank/sumstats/munged/DEPR01.sumstats.gz'))

# Remove rows containing NA values
sumstats<-sumstats[complete.cases(sumstats),]

# Save the reformatted sumstats and compress
write.table(sumstats, '/users/k1806347/brc_scratch/Data/GWAS_sumstats/DEPR01.sumstats.noNA', col.names=T, row.names=F,quote=F)

q()
n

gzip /users/k1806347/brc_scratch/Data/GWAS_sumstats/DEPR01.sumstats.noNA

2.1.2 TWAS analysis

Run TWAS

# Run analysis for each chromomsome and each panel
for chr in $(seq 1 22); do
  for weights in Adrenal_Gland Brain_Amygdala Brain_Anterior_cingulate_cortex_BA24 Brain_Caudate_basal_ganglia Brain_Cerebellar_Hemisphere Brain_Cerebellum Brain_Cortex Brain_Frontal_Cortex_BA9 Brain_Hippocampus Brain_Hypothalamus Brain_Nucleus_accumbens_basal_ganglia Brain_Putamen_basal_ganglia Brain_Substantia_nigra CMC.BRAIN.RNASEQ CMC.BRAIN.RNASEQ_SPLICING NTR.BLOOD.RNAARR Pituitary Thyroid Whole_Blood YFS.BLOOD.RNAARR; do
    qsub -cwd /mnt/lustre/users/k1894478/scripts/Rscript_correct.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/fusion_twas-master/FUSION.assoc_test.R \
      --sumstats /users/k1806347/brc_scratch/Data/GWAS_sumstats/DEPR01.sumstats.noNA.gz \
      --weights /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/${weights}/${weights}.pos \
      --weights_dir /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/${weights} \
      --ref_ld_chr /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/LDREF/1000G.EUR. \
      --out /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr${chr} \
      --chr ${chr} \
      --coloc_P 4.432625e-06 \
      --GWASN 480359
  done
done

# Check each chromosome finished
for weights in Adrenal_Gland Brain_Amygdala Brain_Anterior_cingulate_cortex_BA24 Brain_Caudate_basal_ganglia Brain_Cerebellar_Hemisphere Brain_Cerebellum Brain_Cortex Brain_Frontal_Cortex_BA9 Brain_Hippocampus Brain_Hypothalamus Brain_Nucleus_accumbens_basal_ganglia Brain_Putamen_basal_ganglia Brain_Substantia_nigra CMC.BRAIN.RNASEQ CMC.BRAIN.RNASEQ_SPLICING NTR.BLOOD.RNAARR Pituitary Thyroid Whole_Blood YFS.BLOOD.RNAARR; do
echo $weights
ls /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr* | wc -l  #this tells you how many files there are with the name test.X.chrX
done

# To combine per chromosome results file, without duplicating the header
for weights in Adrenal_Gland Brain_Amygdala Brain_Anterior_cingulate_cortex_BA24 Brain_Caudate_basal_ganglia Brain_Cerebellar_Hemisphere Brain_Cerebellum Brain_Cortex Brain_Frontal_Cortex_BA9 Brain_Hippocampus Brain_Hypothalamus Brain_Nucleus_accumbens_basal_ganglia Brain_Putamen_basal_ganglia Brain_Substantia_nigra CMC.BRAIN.RNASEQ CMC.BRAIN.RNASEQ_SPLICING NTR.BLOOD.RNAARR Pituitary Thyroid Whole_Blood YFS.BLOOD.RNAARR; do
head -n 1 /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr1 > /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.GW      
tail -n +2 -q /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.chr* >> /mnt/lustre/users/k1894478/output_correct/wray_output/test.${weights}.GW    
done

# Create file containing results for all tissues
awk '
    FNR==1 && NR!=1 { while (/TWAS.P/) getline; }
    1 {print}
' /mnt/lustre/users/k1894478/output_correct/wray_output/test.*.GW >/mnt/lustre/users/k1894478/output_correct/wray_output/AllTissues.GW


# Check the .GW files which were created
ls *.GW

# Delete the per chromosome files
for chr in $(seq 1 22); do
  rm /mnt/lustre/users/k1894478/output_correct/wray_output/test.*.chr${chr}
done

rm /mnt/lustre/users/k1894478/output_correct/wray_output/test.*.chr6.MHC

2.2 PsychENCODE SNP-weights


2.2.1 GWAS summary statistics preparation

The PsychENCODE SNP-weights are not restricted to HapMap3 variants, so we need to use unrestricted GWAS summary statistics also. To achieve this I use the FOCUS software munge script.

Show code

# Edit BP column name
zcat /mnt/lustre/groups/ukbiobank/sumstats/cleaned/DEPR01.gz | sed -e 's/ORIGBP/BP/g' > /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP

# Munge
/users/k1806347/brc_scratch/Software/focus.sh munge /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP --output /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus

# Delete temporary file
rm /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP

Show munge log file

## ===================================
##               FOCUS v0.6.10            
## ===================================
## focus munge
##  /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP
##  --output /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus
## 
## Starting log...
## [2019-11-14 10:54:16 - INFO] Interpreting column names as follows:
## [2019-11-14 10:54:16 - INFO] SNP: Variant ID (e.g., rs number)
## [2019-11-14 10:54:16 - INFO] CHR: Chromsome
## [2019-11-14 10:54:16 - INFO] BP: Base position
## [2019-11-14 10:54:16 - INFO] A1: Allele 1, interpreted as ref allele for signed sumstat
## [2019-11-14 10:54:16 - INFO] A2: Allele 2, interpreted as non-ref allele for signed sumstat
## [2019-11-14 10:54:16 - INFO] P: p-Value
## [2019-11-14 10:54:16 - INFO] INFO: INFO score (imputation quality; higher --> better imputation)
## [2019-11-14 10:54:16 - INFO] OR: Odds ratio (1 --> no effect; above 1 --> A1 is risk increasing)
## [2019-11-14 10:54:16 - INFO] N: Sample size
## [2019-11-14 10:54:16 - INFO] Reading sumstats from /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01_BP into memory 5000000 SNPs at a time
## [2019-11-14 10:54:24 - INFO] Reading SNP chunk 1
## [2019-11-14 10:54:44 - INFO] Reading SNP chunk 2
## [2019-11-14 10:54:52 - INFO] Reading SNP chunk 3
## [2019-11-14 10:54:52 - INFO] Done reading SNP chunks
## [2019-11-14 10:54:58 - INFO] Read 10155339 SNPs from --sumstats file
## [2019-11-14 10:54:58 - INFO] Removed 0 SNPs with missing values
## [2019-11-14 10:54:58 - INFO] Removed 2561651 SNPs with INFO <= 0.9
## [2019-11-14 10:54:58 - INFO] Removed 0 SNPs with MAF <= 0.01
## [2019-11-14 10:54:58 - INFO] Removed 0 SNPs with out-of-bounds p-values
## [2019-11-14 10:54:58 - INFO] Removed 1656695 variants that were not SNPs or were strand-ambiguous
## [2019-11-14 10:54:58 - INFO] 5936993 SNPs remain
## [2019-11-14 10:55:04 - INFO] Removed 0 SNPs with duplicated rs numbers (5936993 SNPs remain).
## [2019-11-14 10:55:06 - INFO] Removed 188 SNPs with N < 307422.6666666667 (5936805 SNPs remain)
## [2019-11-14 10:56:30 - INFO] Median value of OR was 1.0, which seems sensible.
## [2019-11-14 10:56:31 - INFO] Writing summary statistics for 5936805 SNPs (5936805 with nonmissing beta) to /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz.
## [2019-11-14 10:57:53 - INFO] METADATA - Mean chi^2 = 1.492
## [2019-11-14 10:57:54 - INFO] METADATA - Lambda GC = 1.383
## [2019-11-14 10:57:54 - INFO] METADATA - Max chi^2 = 79.045
## [2019-11-14 10:57:54 - INFO] METADATA - 2960 Genome-wide significant SNPs (some may have been removed by filtering)
## [2019-11-14 10:57:54 - INFO] Conversion finished

2.2.2 TWAS analysis

Run TWAS

for chr in $(seq 1 22); do
  sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/fusion_twas-master/FUSION.assoc_test.R \
    --sumstats /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz \
    --weights /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights.pos \
    --weights_dir /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights \
    --ref_ld_chr /scratch/groups/biomarkers-brc-mh/Reference_data/1KG_Phase3/PLINK/EUR/EUR_phase3.MAF_001.chr \
    --out /users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr${chr} \
    --chr ${chr} \
    --coloc_P 4.432625e-06 \
    --GWASN 480359
done

Format to match FUSION panel results

library(data.table)

psychENCODE<-NULL
for(i in 1:22){
    if(i == 6){
        tmp1<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr',i))
        tmp2<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr',i,'.MHC'))
        tmp<-rbind(tmp1,tmp2)
    } else {
        tmp<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.chr',i))
    }
    
    psychENCODE<-rbind(psychENCODE, tmp)
}

col_order<-names(psychENCODE)

psychENCODE$PANEL<-as.character(psychENCODE$PANEL)
psychENCODE$PANEL<-'PsychENCODE'

library(biomaRt)
ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl", GRCh=37)
listAttributes(ensembl)
Genes<-getBM(attributes=c('ensembl_gene_id','external_gene_name'), mart = ensembl)

psychENCODE<-merge(psychENCODE, Genes, by.x='ID', by.y='ensembl_gene_id')
psychENCODE$ID<-psychENCODE$external_gene_name
psychENCODE$external_gene_name<-NULL

psychENCODE<-psychENCODE[,col_order, with=F]

write.table(psychENCODE, '/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.GW', col.names=T, row.names=F, quote=F)

2.3 Combine results across FUSION and PsychENCODE panels

Format to match FUSION panel results

library(data.table)

psych<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/MDD_TWAS_PsychENCODE.GW')
fusion<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/AllTissues.GW')

all<-rbind(psych,fusion)

# Write out full results
write.table(all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt', row.names=F, col.names=T, quote=F)

# Write out transcriptome-wide significant results
write.table(all[which(all$TWAS.P < 1.368572e-06),], '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig.txt', row.names=F, col.names=T, quote=F)

dim(all[which(all$TWAS.P < 1.368572e-06),]) # 176 hits
length(unique(all[which(all$TWAS.P < 1.368572e-06),]$ID)) # 94 unique genes

# Check how much PsychENCODE adds
dim(fusion[which(fusion$TWAS.P < 1.368572e-06),]) # 154 hits
length(unique(fusion[which(fusion$TWAS.P < 1.368572e-06),]$ID)) # 84 unique genes

3 Post-TWAS


3.1 Create Manahattan-style plots

Show code

# Manhattan plot based on permutation significance
/users/k1806347/brc_scratch/Software/Rscript_singularity.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-plotter/TWAS-plotter.V1.0.r \
--twas /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
--sig_p 1.368572e-06 \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_Manhattan \
--width 3500 \
--height 2500

#Manhattan plot for high confidence associations
/users/k1806347/brc_scratch/Software/Rscript_singularity.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-plotter/TWAS-plotter.V1.0.r \
--twas /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
--sig_p 3.685926e-08 \
--output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_HighConf_Manhattan \
--width 3500 \
--height 2500

Show Manhattan plots

MDD TWAS Manhattan Plot with transcriptome-wide signficance

MDD TWAS Manhattan Plot with transcriptome-wide signficance


MDD TWAS Manhattan Plot with high-confidence threshold

MDD TWAS Manhattan Plot with high-confidence threshold


3.2 Conditional analysis

Run post_process.R script

# Change directory to location of glist-hg19 file
cd /users/k1806347/brc_scratch/Data/Gene_Locations

mkdir -p /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional

for chr in $(seq 1 22); do

  status=$(awk -v var="${chr}" '$4 == var {print "Present";exit;}' /users/k1806347/brc_scratch/Analyses/Lorenza/PsychENCODE/post-TWAS/MDD_TWAS_AllTissues_TWSig.txt )
  
  if [ "$status" == "Present" ]; then
    sbatch -p brc,shared --mem 25G -n 1 /users/k1806347/brc_scratch/Software/Rscript_singularity.sh /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/fusion_twas-master/FUSION.post_process.R \
      --input /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig.txt \
      --sumstats /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz \
      --report \
      --ref_ld_chr /scratch/groups/biomarkers-brc-mh/Reference_data/1KG_Phase3/PLINK/EUR/EUR_phase3.MAF_001.chr \
      --out /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/test.cond.chr${chr} \
      --chr ${chr} \
      --plot \
      --plot_legend all \
      --save_loci \
      --locus_win 500000
  fi

done

3.3 Process TWAS results

Clean the TWAS results

###
# Clean file PANEL names 
###

rm(list=ls())
library(data.table)
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig.txt")
twas <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt")

twas_sign$BEST.GWAS.P<-2*pnorm(-abs(twas_sign$BEST.GWAS.Z))
sum(twas_sign$BEST.GWAS.P > 5e-8) # 63
sum(twas_sign$BEST.GWAS.P < 5e-8) # 113

str(twas_sign)
str(twas)

#clean the PANEL names of the output df containing results on all tested features
twas$PANEL_clean<-gsub('_',' ',twas$PANEL)
twas$PANEL_clean<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',twas$PANEL_clean)
twas$PANEL_clean<-gsub('SPLICING','Splicing',twas$PANEL_clean)
twas$PANEL_clean<-gsub('NTR.BLOOD.RNAARR','NTR Blood',twas$PANEL_clean)
twas$PANEL_clean<-gsub('YFS.BLOOD.RNAARR','YFS Blood',twas$PANEL_clean)
twas$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas$PANEL)]<-paste0('GTEx ',twas$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas$PANEL)])
#to add gtex to each of the snp weights which don't have CMC NTR or YFS in front
twas$PANEL_clean<-gsub('Brain', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('Anterior cingulate cortex', 'ACC', twas$PANEL_clean)
twas$PANEL_clean <- gsub('basal ganglia', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('BA9', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('BA24', '', twas$PANEL_clean)
twas$PANEL_clean <- gsub('  ', ' ', twas$PANEL_clean)

# Shorten panel name to plot easily
twas$PANEL_clean_short<-substr(twas$PANEL_clean, start = 1, stop = 25)  #start the name at the first character and stop at the 25th
twas$PANEL_clean_short[nchar(twas$PANEL_clean) > 25]<-paste0(twas$PANEL_clean_short[nchar(twas$PANEL_clean) > 25], "...")

#do the same for the output file with sign features only
twas_sign$PANEL_clean<-gsub('_',' ',twas_sign$PANEL)
twas_sign$PANEL_clean<-gsub('CMC.BRAIN.RNASEQ','CMC DLPFC',twas_sign$PANEL_clean)
twas_sign$PANEL_clean<-gsub('SPLICING','Splicing',twas_sign$PANEL_clean)
twas_sign$PANEL_clean<-gsub('NTR.BLOOD.RNAARR','NTR Blood',twas_sign$PANEL_clean)
twas_sign$PANEL_clean<-gsub('YFS.BLOOD.RNAARR','YFS Blood',twas_sign$PANEL_clean)
twas_sign$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas_sign$PANEL)]<-paste0('GTEx ',twas_sign$PANEL_clean[!grepl('CMC|NTR|YFS|PsychENCODE', twas_sign$PANEL)])
#to add gtex to each of the snp weights which don't have CMC NTR or YFS in front
twas_sign$PANEL_clean<-gsub('Brain', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('Anterior cingulate cortex', 'ACC', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('basal ganglia', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('BA9', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('BA24', '', twas_sign$PANEL_clean)
twas_sign$PANEL_clean <- gsub('  ', ' ', twas_sign$PANEL_clean)

# Shorten panel name to plot easily
twas_sign$PANEL_clean_short<-substr(twas_sign$PANEL_clean, start = 1, stop = 25)  #start the name at the first character and stop at the 25th
twas_sign$PANEL_clean_short[nchar(twas_sign$PANEL_clean) > 25]<-paste0(twas_sign$PANEL_clean_short[nchar(twas_sign$PANEL_clean) > 25], "...")

#check the variables
str(twas)
str(twas_sign)

###
# Deal with missingness and subset for the relevant cols only
###

##TWAS df
#exclude missings
twas<-twas[!is.na(twas$TWAS.Z),]
twas<-twas[!is.na(twas$TWAS.P),]

#subset columns needed 
twas_sub <- twas[,c('FILE', 'ID','PANEL', 'PANEL_clean_short','PANEL_clean','CHR','P0', 'P1', 'TWAS.Z', 'TWAS.P', 'COLOC.PP0', 'COLOC.PP1', 'COLOC.PP2', 'COLOC.PP3', 'COLOC.PP4')]
str(twas_sub)

##TWAS sign df
#exclude missings
twas_sign<-twas_sign[!is.na(twas_sign$TWAS.Z),]
twas_sign<-twas_sign[!is.na(twas_sign$TWAS.P),]

#subset columns needed 
twas_sign_sub <- twas_sign[,c('FILE', 'ID','PANEL', 'PANEL_clean_short','PANEL_clean','CHR','P0', 'P1', 'TWAS.Z', 'TWAS.P', 'COLOC.PP0', 'COLOC.PP1', 'COLOC.PP2', 'COLOC.PP3', 'COLOC.PP4')]
str(twas_sign_sub)

###
# Update positions
###

# Rationale: the positions in the output files created by FUSION are rounded, thus not completely accurate. 
# Therefore, we need to update the positions (P0 and P1) based on the pos files in Rosalind. This needs to be done on Putty though. A new file will be saved and reopened here. 

setwd('/mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/')

weights<-c('Adrenal_Gland', 'Brain_Amygdala', 'Brain_Anterior_cingulate_cortex_BA24', 'Brain_Caudate_basal_ganglia', 'Brain_Cerebellar_Hemisphere', 'Brain_Cerebellum', 'Brain_Cortex', 'Brain_Frontal_Cortex_BA9', 'Brain_Hippocampus', 'Brain_Hypothalamus', 'Brain_Nucleus_accumbens_basal_ganglia', 'Brain_Putamen_basal_ganglia', 'Brain_Substantia_nigra', 'CMC.BRAIN.RNASEQ', 'CMC.BRAIN.RNASEQ_SPLICING', 'NTR.BLOOD.RNAARR', 'Pituitary', 'Thyroid', 'Whole_Blood', 'YFS.BLOOD.RNAARR')

#Get all pos files within the SNP-weight sets and bind them 
FUSION_pos<-NULL
for(i in weights){
FUSION_pos_temp<-read.table(paste(i, '/',i, '.pos',sep=''), header=T, stringsAsFactors=F)   #repeating i twice with / in the middle is to get one folder further 
FUSION_pos<-rbind(FUSION_pos, FUSION_pos_temp)
}

PsychENCODE_pos<-read.table('/scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights.pos', header=T, stringsAsFactors=F)
PsychENCODE_pos$PANEL<-'PsychENCODE'

# Combine pos files
FUSION_pos<-rbind(FUSION_pos, PsychENCODE_pos)

write.table(FUSION_pos,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos', col.names=T, row.names=F, quote=F)

str(FUSION_pos)   #97733 observations of 7 variables (PANEL, WGT, ID, CHR, P0, P1, N)

###
# Merge the pos file with the twas_sub and twas_sign_sub df
###

#the pos file and the output file do not have the same columns with the same information. We therefore need to slightly modify the TWAS columns 
twas_sub$tmp<-gsub('/mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/','',twas_sub$FILE)
twas_sub$tmp<-gsub('/scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/','',twas_sub$tmp)
#to delete the full pathway of the file and just keep the important information 
twas_sub$PANEL<-sub('/.*','', twas_sub$tmp)
twas_sub$Feature<-gsub('.*/','',twas_sub$tmp)
twas_sub$WGT<-paste0(twas_sub$PANEL, '/', twas_sub$Feature)
twas_sub$PANEL<-NULL
twas_sub$tmp<-NULL
twas_sub$Feature<-NULL

twas_sub[order(twas_sub$WGT), ]
FUSION_pos[order(FUSION_pos$WGT), ]

#merge
twas_sub_correct <- merge(twas_sub, FUSION_pos, by="WGT")

#check
head(twas_sub_correct)

#clean
twas_sub_correct$ID.y<-NULL
colnames(twas_sub_correct)
names(twas_sub_correct)[3]<-'ID'   #to change the name  of IDx to ID
head(twas_sub_correct)
  
#repeat everything for the twas_sign_sub file
twas_sign_sub$tmp<-gsub('/mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/','',twas_sign_sub$FILE)
twas_sign_sub$tmp<-gsub('/scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/','',twas_sign_sub$tmp)

#to delete the full pathway of the file and just keep the important information 
twas_sign_sub$PANEL<-sub('/.*','', twas_sign_sub$tmp)
twas_sign_sub$Feature<-gsub('.*/','',twas_sign_sub$tmp)
twas_sign_sub$WGT<-paste0(twas_sign_sub$PANEL, '/', twas_sign_sub$Feature)
twas_sign_sub$PANEL<-NULL
twas_sign_sub$tmp<-NULL
twas_sign_sub$Feature<-NULL

# twas_sign_sub[order(twas_sign_sub$WGT), ]

#merge
twas_sign_sub_correct <- merge(twas_sign_sub, FUSION_pos, by="WGT")

#check
head(twas_sign_sub_correct)

#clean
twas_sign_sub_correct$ID.y<-NULL
colnames(twas_sign_sub_correct)
names(twas_sign_sub_correct)[3]<-'ID'   #to change the name  of IDx to ID
 
head(twas_sign_sub_correct)
dim(twas_sign_sub_correct)
dim(twas_sub_correct)

###
# Clean output files for future scripts
###

#clean both output files to have clean outputs to use in future scripts

#twas sign sub correct df
colnames(twas_sign_sub_correct)

names(twas_sign_sub_correct)[6] <- "CHR"
twas_sign_sub_correct$CHR.y <- NULL
colnames(twas_sign_sub_correct)
names(twas_sign_sub_correct)[17] <- "P0" #turn POy into P0 - nb P0y is the one withh the more accurate positions
names(twas_sign_sub_correct)[18] <- "P1" 

twas_sign_sub_correct$P0.x <- NULL
twas_sign_sub_correct$P1.x <- NULL


colnames(twas_sign_sub_correct)
twas_sign_sub_correct$N <- NULL
str(twas_sign_sub_correct)

#change the variable types for those which are wrong 

#turn PO and P1 into numerical variables
twas_sign_sub_correct$P0 <- as.numeric(as.character(twas_sign_sub_correct$P0))
twas_sign_sub_correct$P1 <- as.numeric(as.character(twas_sign_sub_correct$P1))

str(twas_sign_sub_correct)

#twas_sub correct df
colnames(twas_sub_correct)

names(twas_sub_correct)[6]<- "CHR"
names(twas_sub_correct)[18]<- "P0"
names(twas_sub_correct)[19]<- "P1"
twas_sub_correct$CHR.y <- NULL
twas_sub_correct$P0.x <- NULL
twas_sub_correct$P1.x <- NULL

colnames(twas_sub_correct)
twas_sub_correct$N <- NULL

str(twas_sub_correct)

#change variable type for PO and P1
twas_sub_correct$P0 <- as.numeric(as.character(twas_sub_correct$P0))
twas_sub_correct$P1 <- as.numeric(as.character(twas_sub_correct$P1))

#save 
write.table(twas_sub_correct, file = "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt", sep = " ", col.names = T, row.names = F)
write.table(twas_sign_sub_correct, file = "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt", sep = " ", col.names = T, row.names=F)

q()
n

Create a table with the transcriptome-wide significant findings

rm(list=ls())
library(data.table)

twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")

str(twas_sign)
twas_sign$CHR <- as.numeric(as.character(twas_sign$CHR))
twas_sign$P0 <- as.numeric(as.character(twas_sign$P0))
twas_sign$P1 <- as.numeric(as.character(twas_sign$P1))

twas_sign <- twas_sign[order(twas_sign$CHR, twas_sign$P0), ]

twas_sign$Location <- paste0('chr',twas_sign$CHR,':',twas_sign$P0,'-',twas_sign$P1) 

colnames(twas_sign)

library(dplyr)
library(tibble)
twas_sign <- as_data_frame(twas_sign)

col_order <- c("Location", "ID", "PANEL_clean_short", "TWAS.Z", "TWAS.P")

twas_sign_final <- twas_sign[, col_order]

write.csv(twas_sign_final, "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.brief.csv", row.names=F)

twas_sign_final

Show transcriptome-wide significant table

Transcriptome-wide significant associations with Major Depression
Location ID PANEL_clean_short TWAS.Z TWAS.P
chr1:8412457-8877702 RERE GTEx Whole Blood -5.095707 3.47e-07
chr1:8412457-8877702 RERE YFS Blood -5.310078 1.10e-07
chr1:8484705-8494898 RP5-1115A15.1 GTEx Thyroid -5.175240 2.28e-07
chr1:8484705-8494898 RP5-1115A15.1 GTEx Whole Blood -4.866386 1.14e-06
chr1:36884051-36884179 SNORA63 GTEx Nucleus accumbens 4.848870 1.24e-06
chr1:71861623-72748417 NEGR1 GTEx Caudate 5.780100 7.47e-09
chr1:71861623-72748417 NEGR1 GTEx Putamen 5.548510 2.88e-08
chr1:71861623-72748417 NEGR1 GTEx Whole Blood 8.760622 1.94e-18
chr1:72767155-72767512 RPL31P12 GTEx Cerebellar Hemispher… -7.785520 6.94e-15
chr1:72767155-72767512 RPL31P12 GTEx Cerebellum -7.708820 1.27e-14
chr1:72767155-72767512 RPL31P12 PsychENCODE -7.742756 9.73e-15
chr1:175873898-175889649 RP11-318C24.2 GTEx Thyroid -5.027510 4.97e-07
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing -4.958690 7.10e-07
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing 5.039850 4.66e-07
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing -5.005960 5.56e-07
chr1:181452685-181775921 CACNA1E CMC DLPFC Splicing -4.989390 6.06e-07
chr1:197473878-197744623 DENND1B CMC DLPFC 4.848374 1.24e-06
chr1:197473878-197744623 DENND1B CMC DLPFC Splicing -5.421950 5.90e-08
chr1:197473878-197744623 DENND1B CMC DLPFC Splicing 5.018050 5.22e-07
chr2:58386377-58468515 FANCL CMC DLPFC -5.183180 2.18e-07
chr2:58386377-58468515 FANCL CMC DLPFC Splicing 4.897476 9.71e-07
chr2:197831741-198175897 ANKRD44 YFS Blood -5.690140 1.27e-08
chr2:198254508-198299815 SF3B1 GTEx Hypothalamus 5.214900 1.84e-07
chr3:44481261-44561226 ZNF445 CMC DLPFC -5.103280 3.34e-07
chr4:41937137-41962589 TMEM33 PsychENCODE 4.837418 1.32e-06
chr4:41983713-41988476 DCAF4L1 GTEx Thyroid -5.128000 2.93e-07
chr4:41990758-41991254 RP11-814H16.2 GTEx Cerebellar Hemispher… 5.009600 5.45e-07
chr4:41992489-42092474 SLC30A9 GTEx Amygdala -5.253400 1.49e-07
chr4:41992489-42092474 SLC30A9 GTEx ACC -5.001690 5.68e-07
chr4:41992489-42092474 SLC30A9 GTEx Caudate -4.854800 1.21e-06
chr4:41992489-42092474 SLC30A9 GTEx Cortex -5.774530 7.72e-09
chr4:41992489-42092474 SLC30A9 GTEx Hypothalamus -5.085140 3.67e-07
chr4:41992489-42092474 SLC30A9 GTEx Nucleus accumbens -5.602700 2.11e-08
chr4:41992489-42092474 SLC30A9 PsychENCODE -5.259200 1.45e-07
chr5:87564712-87732502 TMEM161B-AS1 PsychENCODE 6.091010 1.12e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Adrenal Gland 5.360090 8.32e-08
chr5:87564888-87732502 TMEM161B-AS1 GTEx Amygdala 6.118500 9.45e-10
chr5:87564888-87732502 TMEM161B-AS1 GTEx ACC 6.445500 1.15e-10
chr5:87564888-87732502 TMEM161B-AS1 GTEx Caudate 6.282167 3.34e-10
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cerebellar Hemispher… 6.011700 1.84e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cerebellum 6.053050 1.42e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cortex 6.021420 1.73e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Frontal Cortex 6.720000 1.82e-11
chr5:87564888-87732502 TMEM161B-AS1 GTEx Hypothalamus 5.875800 4.21e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Nucleus accumbens 6.010490 1.85e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Putamen 6.372050 1.87e-10
chr5:87564888-87732502 TMEM161B-AS1 GTEx Substantia nigra 6.057270 1.38e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Pituitary 6.048500 1.46e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Thyroid 5.889760 3.87e-09
chr5:87564888-87732502 TMEM161B-AS1 GTEx Whole Blood 5.526440 3.27e-08
chr5:87729709-87794514 CTC-498M16.4 GTEx Substantia nigra 5.403610 6.53e-08
chr5:87988462-87989789 CTC-467M3.3 GTEx ACC -5.813700 6.11e-09
chr5:87988462-87989789 CTC-467M3.3 GTEx Cerebellar Hemispher… -5.861000 4.60e-09
chr5:87988462-87989789 CTC-467M3.3 GTEx Cortex -6.510990 7.47e-11
chr5:87988462-87989789 CTC-467M3.3 GTEx Frontal Cortex -7.091600 1.33e-12
chr5:87988462-87989789 CTC-467M3.3 PsychENCODE -6.097890 1.07e-09
chr5:140024947-140027370 NDUFA2 CMC DLPFC 5.190020 2.10e-07
chr5:140201222-140203811 PCDHA5 GTEx Thyroid -5.402970 6.55e-08
chr5:140220907-140223351 PCDHA8 GTEx Cerebellar Hemispher… -4.980100 6.36e-07
chr6:26188921-26189323 HIST1H4D NTR Blood -4.987600 6.11e-07
chr6:26365386-26378540 BTN3A2 NTR Blood 5.326600 1.00e-07
chr6:26365387-26378546 BTN3A2 GTEx Cerebellar Hemispher… 5.188200 2.12e-07
chr6:26365387-26378546 BTN3A2 GTEx Hippocampus 4.963000 6.96e-07
chr6:26365387-26378546 BTN3A2 GTEx Pituitary 5.898930 3.66e-09
chr6:26365387-26378546 BTN3A2 GTEx Thyroid 5.481600 4.22e-08
chr6:26365387-26378546 BTN3A2 GTEx Whole Blood 5.086960 3.64e-07
chr6:26538633-26546482 HMGN4 GTEx Cerebellum 5.395400 6.84e-08
chr6:27215480-27224250 PRSS16 GTEx Cerebellar Hemispher… -4.891200 1.00e-06
chr6:27215480-27224250 PRSS16 GTEx Cerebellum -4.947900 7.50e-07
chr6:27215480-27224250 PRSS16 GTEx Frontal Cortex -5.045000 4.54e-07
chr6:27215480-27224250 PRSS16 GTEx Pituitary -5.916080 3.30e-09
chr6:27215480-27224250 PRSS16 GTEx Whole Blood -5.335920 9.51e-08
chr6:27325604-27339304 ZNF204P GTEx Adrenal Gland -5.032700 4.84e-07
chr6:27371789-27374743 RP1-153G14.4 GTEx Hippocampus 5.354000 8.60e-08
chr6:27418522-27440897 ZNF184 GTEx Caudate -6.325200 2.53e-10
chr6:27418522-27440897 ZNF184 GTEx Hypothalamus -4.952200 7.34e-07
chr6:27840926-27841289 HIST1H4L NTR Blood 4.870800 1.11e-06
chr6:28058932-28061442 ZSCAN12P1 PsychENCODE 6.268010 3.66e-10
chr6:28058932-28061442 ZSCAN12P1 GTEx Whole Blood -4.936930 7.94e-07
chr6:28083406-28084329 RP1-265C24.5 GTEx Hippocampus 5.532000 3.16e-08
chr6:28092338-28097860 ZSCAN16 YFS Blood -6.109000 1.00e-09
chr6:28192664-28201260 ZSCAN9 GTEx Cerebellum -5.307800 1.11e-07
chr6:28192664-28201260 ZSCAN9 GTEx Hippocampus -6.017000 1.77e-09
chr6:28192664-28201260 ZSCAN9 GTEx Pituitary -6.159020 7.32e-10
chr6:28227098-28228736 NKAPL PsychENCODE 5.002860 5.65e-07
chr6:28234788-28245974 RP5-874C20.3 GTEx Adrenal Gland 5.094600 3.49e-07
chr6:28234788-28245974 RP5-874C20.3 GTEx Cerebellum 5.062800 4.13e-07
chr6:28234788-28245974 RP5-874C20.3 GTEx Hippocampus 5.198000 2.01e-07
chr6:28234788-28245974 RP5-874C20.3 GTEx Putamen 5.739000 9.52e-09
chr6:28234788-28245974 RP5-874C20.3 GTEx Thyroid 5.338400 9.38e-08
chr6:28234788-28245974 RP5-874C20.3 GTEx Whole Blood 5.662330 1.49e-08
chr6:28249314-28270326 PGBD1 GTEx Cerebellar Hemispher… -6.313100 2.74e-10
chr6:28292470-28324048 ZSCAN31 GTEx Amygdala -5.084150 3.69e-07
chr6:28317691-28336947 ZKSCAN3 GTEx Amygdala 4.949900 7.43e-07
chr6:28317691-28336947 ZKSCAN3 GTEx Hippocampus 4.951000 7.37e-07
chr6:28317691-28336947 ZKSCAN3 GTEx Thyroid 6.093300 1.11e-09
chr6:28399707-28411279 ZSCAN23 GTEx Hypothalamus -5.777500 7.58e-09
chr6:28399707-28411279 ZSCAN23 GTEx Putamen -4.894000 9.90e-07
chr6:28399707-28411279 ZSCAN23 GTEx Pituitary -4.953290 7.30e-07
chr6:30644166-30655672 PPP1R18 GTEx Adrenal Gland 4.910200 9.10e-07
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.299700 1.16e-07
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.067100 4.04e-07
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing 4.936600 7.95e-07
chr6:30695486-30710510 FLOT1 GTEx Cerebellum -5.299000 1.16e-07
chr6:30695486-30710510 FLOT1 GTEx Pituitary -5.253270 1.49e-07
chr6:30695486-30710510 FLOT1 GTEx Thyroid -5.557400 2.74e-08
chr6:30881982-30894236 VARS2 GTEx Cortex 5.922000 3.18e-09
chr6:30881982-30894236 VARS2 GTEx Whole Blood 6.323130 2.56e-10
chr6:31255287-31256741 WASF5P GTEx Pituitary -5.156240 2.52e-07
chr6:31368479-31445283 HCP5 GTEx Thyroid 6.400800 1.55e-10
chr6:31462658-31478901 MICB GTEx Thyroid -5.557000 2.74e-08
chr6:31606805-31620482 BAG6 CMC DLPFC Splicing -5.580000 2.40e-08
chr6:31694815-31698357 DDAH2 GTEx Frontal Cortex 5.409500 6.32e-08
chr6:31694816-31698039 DDAH2 CMC DLPFC 5.344500 9.07e-08
chr6:99817347-99842082 COQ3 CMC DLPFC Splicing 5.146560 2.65e-07
chr6:105404922-105531207 LIN28B CMC DLPFC -5.232050 1.68e-07
chr6:105404923-105531207 LIN28B PsychENCODE -5.105689 3.30e-07
chr6:105584224-105617820 BVES-AS1 GTEx Amygdala -5.578300 2.43e-08
chr7:12250867-12282993 TMEM106B GTEx Adrenal Gland 5.505026 3.69e-08
chr7:12250867-12282993 TMEM106B PsychENCODE -5.790690 7.01e-09
chr7:12250867-12282993 TMEM106B GTEx Whole Blood 5.531000 3.18e-08
chr7:12250867-12276886 TMEM106B YFS Blood 5.373600 7.72e-08
chr7:24836158-25021253 OSBPL3 GTEx Pituitary -5.622890 1.88e-08
chr8:52232136-52722005 PXDNL CMC DLPFC 5.887460 3.92e-09
chr8:61297147-61429354 RP11-163N6.2 GTEx Thyroid -5.336530 9.47e-08
chr9:126605315-126605965 PIGFP2 PsychENCODE -5.305600 1.12e-07
chr11:57067112-57092426 TNKS1BP1 GTEx Adrenal Gland 4.922610 8.54e-07
chr11:57405497-57420263 AP000662.4 GTEx Thyroid -4.980256 6.35e-07
chr11:57424488-57429340 CLP1 GTEx Whole Blood 5.195860 2.04e-07
chr11:61535973-61560274 TMEM258 PsychENCODE 5.021730 5.12e-07
chr11:113280318-113346111 DRD2 GTEx Frontal Cortex -5.073787 3.90e-07
chr13:53602875-53626196 OLFM4 CMC DLPFC 5.091290 3.56e-07
chr14:42057064-42074059 CTD-2298J14.2 GTEx Thyroid -5.678860 1.36e-08
chr14:42076773-42373752 LRFN5 GTEx Cerebellar Hemispher… 5.423400 5.85e-08
chr14:42076773-42373752 LRFN5 GTEx Cerebellum 5.597540 2.17e-08
chr14:59951161-59971429 JKAMP GTEx Thyroid -5.125100 2.97e-07
chr14:59971779-60043549 CCDC175 GTEx Thyroid -5.478850 4.28e-08
chr14:60062693-60337557 RTN1 CMC DLPFC Splicing -4.874920 1.09e-06
chr14:60062695-60337684 RTN1 GTEx Thyroid -5.348450 8.87e-08
chr14:64319682-64693151 SYNE2 NTR Blood 5.609528 2.03e-08
chr14:64550950-64770377 ESR2 GTEx Pituitary -5.982300 2.20e-09
chr14:64550950-64770377 ESR2 GTEx Whole Blood -5.655371 1.56e-08
chr14:75120140-75179818 AREL1 PsychENCODE -5.015110 5.30e-07
chr14:75319736-75330537 PROX2 GTEx Thyroid -5.758100 8.51e-09
chr14:75348593-75370450 DLST CMC DLPFC 4.981400 6.31e-07
chr14:75348594-75370448 DLST PsychENCODE 5.089700 3.59e-07
chr14:75370656-75389188 RPS6KL1 CMC DLPFC Splicing -5.023810 5.07e-07
chr14:75370657-75390099 RPS6KL1 PsychENCODE -4.952550 7.32e-07
chr14:103878456-103879098 RP11-600F24.2 PsychENCODE 5.185660 2.15e-07
chr14:103985996-103989448 CKB YFS Blood 5.346000 8.99e-08
chr14:103995508-104003410 TRMT61A CMC DLPFC 5.051300 4.39e-07
chr14:103995521-104003410 TRMT61A GTEx Whole Blood 4.977593 6.44e-07
chr14:104019758-104028214 RP11-894P9.2 GTEx Thyroid -5.462560 4.69e-08
chr14:104153913-104154464 RP11-73M18.6 PsychENCODE 5.031320 4.87e-07
chr14:104160897-104161507 RP11-73M18.7 PsychENCODE 4.856130 1.20e-06
chr14:104162690-104163500 RP11-73M18.8 GTEx Amygdala 5.142000 2.72e-07
chr14:104177607-104179149 AL049840.1 GTEx Cerebellum 5.029540 4.92e-07
chr14:104177607-104179149 AL049840.1 GTEx Cortex 5.143620 2.69e-07
chr14:104179904-104180441 RP11-73M18.9 GTEx Cortex 4.977330 6.45e-07
chr14:104179904-104180586 RP11-73M18.9 PsychENCODE 4.830100 1.36e-06
chr16:72146056-72210777 PMFBP1 PsychENCODE -5.160620 2.46e-07
chr17:27400528-27507430 MYO18A GTEx Adrenal Gland -5.128570 2.92e-07
chr17:27401933-27405875 TIAF1 GTEx Adrenal Gland -5.361200 8.27e-08
chr17:65520597-65521538 CTD-2653B5.1 PsychENCODE 5.105730 3.30e-07
chr18:52385091-52562747 RAB27B PsychENCODE 5.012900 5.36e-07
chr18:52495707-52562747 RAB27B CMC DLPFC Splicing 4.843190 1.28e-06
chr20:47835831-47860614 DDX27 CMC DLPFC 4.836260 1.32e-06
chr22:41165634-41215403 SLC25A17 GTEx Nucleus accumbens 5.076990 3.83e-07
chr22:41165634-41215403 SLC25A17 GTEx Thyroid 4.896100 9.78e-07
chr22:41253088-41351450 XPNPEP3 GTEx Frontal Cortex 4.951000 7.38e-07
chr22:41258260-41363888 XPNPEP3 CMC DLPFC 5.110000 3.21e-07
chr22:41487790-41576081 EP300 GTEx Cerebellum 5.493900 3.93e-08
chr22:41487790-41576081 EP300 YFS Blood 5.059100 4.21e-07
chr22:41641614-41682216 RANGAP1 CMC DLPFC Splicing 5.240100 1.61e-07
chr22:41641615-41682255 RANGAP1 PsychENCODE -5.575273 2.47e-08
chr22:41697526-41756151 ZC3H7B GTEx Cerebellum 5.729100 1.01e-08

3.4 Plot TWAS results

Create QQ-plot and histogram of p-values

####
#QQplot
###

#load file with all hits - not just sign, ones
twas_sub_correct <- read.table("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt", header=T, stringsAsFactors = F)

ggd.qqplot = function(pvector, main=NULL, ...) {
  o = -log10(sort(pvector,decreasing=F))
  e = -log10( 1:length(o)/length(o) )
  plot(e,o,pch=19,cex=1, main=main, ...,
       xlab=expression(Expected~~-log[10](italic(p))),
       ylab=expression(Observed~~-log[10](italic(p))),
       xlim=c(0,max(e)), ylim=c(0,max(o)))
  lines(e,e,col="red")
}

pvalues <- twas_sub_correct$TWAS.P

# Add a title
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_QQplot.png",width = 2000, height = 2000, units = "px", res=300)
ggd.qqplot(pvalues, "QQ-plot of TWAS p-values")
dev.off() 

###
# Histogram of p-values
###

library(ggplot2)

## HISTOGRAM OF P-VALUES
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_pValHist.png",width = 2000, height = 2000, units = "px", res=300)
hist(twas_sub_correct$TWAS.P,
     main = "Histogram of TWAS p-values",  
     xlab ="P-values", 
     ylab = "Frequency")
dev.off()

Show plots

MDD TWAS QQ-plot

MDD TWAS QQ-plot

As shown below in the QQ-plot, our p-values were smaller than expected, indicating the presence of multiple significant associations. Inflation is present, but this expected due to the polygenicity of Major Depression and the the correlation between predicted expression of genes.


MDD TWAS P-value histogram

MDD TWAS P-value histogram

As shown in the histogram of p-values, our p-values followed a normal distribution as evidenced in the bottom of the graph where a similar amount of p-values is present. Additionally, a peak in correspondence to very small p-values is present at the top of the graph, indicating the presence of signal for our alternative hypothesis.

Create heatmaps for shared and unique associations

# Given the high number of hits we identified (N=177), from 91 unique genes, a single heatmap representing all of such genes cannot be created. Therefore, we depicted our results in two heatmaps: 1) heatmap of genes differentially expressed across multiple SNP-weight sets & 2) heatmap of genes differentially across a single SNP-weight

rm(list=ls())
library(data.table)
library(ggplot2)
library(cowplot)

#load data
twas <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt")
head(twas)

# Extract only certain columns
twas_sub <- twas[,c('ID','PANEL_clean_short','CHR','P0','P1','TWAS.Z', 'TWAS.P')]
str(twas_sub)
str(twas)

#filter for sign. gene IDs only 
sign_feat<-twas_sub[twas_sub$TWAS.P < 1.368572e-06,'ID']  #to get a vector with the gene IDs of the significant features
str(sign_feat)  #find 176 features as supposed to - genes are repeated though --> you need single gene IDs

sign_genes<-unique(sign_feat)  #to identify unique gene IDs
str(sign_genes)  #94 gene IDs as expected

twas_sub[order(twas_sub$ID), ]
sign_genes[order(sign_genes$ID), ]

twas_sub <- twas_sub[(twas_sub$ID %in% sign_genes$ID), ]
str(twas_sub)  #611 observations where each observation corresponds to an ID in the sign-feat vector

#prepare a vector of gene IDs which are duplicated
duplicates_df<-sign_feat[duplicated(sign_feat)] 
duplicates <- duplicates_df$ID

# The following code is necessary to depict results from the CMC DLPFC splicing panel 
# Since in RNA-seq splicing multiple transcripts of the same gene are generally tested and we can depict just one in the heatmap, we need to pick the most significant one. This is done below. 

### 
# RETAIN THE MOST SIGN. FEATURE FROM THE CMC BRAIN SPLICING RESULTS
###

twas_sub <- twas_sub[order(twas_sub$TWAS.P), ] #we order by p-value to make sure that the most significant 
#CMC DLPFC feature for a given gene is kept and that its duplicates (which are less sign.) are excluded
head(twas_sub)
tail(twas_sub)

library(dplyr)
twas_sub  <- twas_sub %>% distinct(ID, PANEL_clean_short, .keep_all = T)  #to get rid of rows which contain duplicates based on the ID and PANEL cols 
#in our case, this is just for duplicates in the cmc brain splicing weights with the same gene id

twas_sub_temp <- twas_sub[order(twas_sub$ID), ] #to check that it worked 
#there should be just one of the same gene id from brain seq splicing weights
#this is the case, with the mmost sign. one being kept. 
#NB 43 gene IDs from the same snp weight were gotten rid of in this df for a total of 568 features instead of 611 

####
# Heatmap of genes differentially expressed across multiple SNP-weight sets
####
###
# make a list of features significant in multiple tissues
###

#order files  
twas_sub2<-twas_sub[order(twas_sub$ID),] 
duplicates<-sort(duplicates, decreasing = FALSE)   

#filter the twas_sub datatable by the duplicates vector to obtain a dt with features expressed across diff. tissues only (i.e. no unique features)
twas_sub2<-twas_sub[(twas_sub$ID %in% duplicates), ] #283 obs.
str(twas_sub2)

# Sort the data.frame by CHR and P0 
twas_sub2<-twas_sub2[order(twas_sub2$CHR,twas_sub2$P0),]

# Make ID a factor for plotting where unique gene IDs are the levels/categories of such factor 
twas_sub2$ID<-factor(twas_sub2$ID, levels=unique(twas_sub2$ID))
str(twas_sub2) #there are 36 levels (i.e. 36 unique genes differentially expressed across multiple weights)

#create a vector with all TWAS.Z values
TWAS.Z <- twas_sub2$TWAS.Z

twas_sub2_unique<-twas_sub2[!duplicated(twas_sub2$ID),]
vline_1<-min(which(twas_sub2_unique$CHR == 6 & twas_sub2_unique$P0 > 26e6 & twas_sub2_unique$P1 < 34e6))
vline_2<-max(which(twas_sub2_unique$CHR == 6 & twas_sub2_unique$P0 > 26e6 & twas_sub2_unique$P1 < 34e6))

#create the heatmap
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap.shared.png",width = 3500, height = 1750, units = "px", res=300)

ggplot(data = twas_sub2, aes(x = ID, y = PANEL_clean_short)) +
  #genes as x axis, panel as y axis  
  theme_bw()    +    #saying that there will be grid lines
  geom_tile(aes(fill = TWAS.Z), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z,1)), color="black", size=3) +
  labs(title="Genes differentially expressed across multiple SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)

#title and labels shown for the x and y axes
dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

#### 
# Heatmap of genes differentially expressed in one SNP-weight 
####

#create a dt with the duplicates removed (!), so that we obtain only unique features
twas_unique<-twas_sub[(!twas_sub$ID %in% duplicates), ]  
str(twas_unique)   #we get 285 features from differentially expressed genes uniquely differentially expressed in one tissue

# Sort the data.frame by CHR and P0
twas_unique<-twas_unique[order(twas_unique$CHR,twas_unique$P0),]  #it's important to order by both
#if you order by pos only, the chr11 stuff will come first!

# Make ID a factor for plotting where unique gene IDs are the levels/categories of such factor 
twas_unique$ID<-factor(twas_unique$ID, levels=unique(twas_unique$ID))
str(twas_unique)

#create a vector with all TWAS.Z values
TWAS.Z <- twas_unique$TWAS.Z

twas_unique_unique<-twas_unique[!duplicated(twas_unique$ID),]
vline_1<-min(which(twas_unique_unique$CHR == 6 & twas_unique_unique$P0 > 26e6 & twas_unique_unique$P1 < 34e6))
vline_2<-max(which(twas_unique_unique$CHR == 6 & twas_unique_unique$P0 > 26e6 & twas_unique_unique$P1 < 34e6))

#create the heatmap
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap.unique.png",width = 4650, height = 1750, units = "px", res=300)

ggplot(data = twas_unique, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +    
  geom_tile(aes(fill = TWAS.Z), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in single SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)

dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

Create heatmaps for tissue groups

# Heatmaps for groups of tissues were made to show the overlap across SNP-weight panels. 

rm(list=ls())
library(data.table)
library(ggplot2)
library(cowplot)

###
# Load and prepare data
###

#load
twas <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_CLEAN.txt")

#subset columns needed 
twas_sub <- twas[,c('ID','PANEL', 'PANEL_clean_short', 'CHR','P0', 'P1', 'TWAS.Z', 'TWAS.P')]
str(twas_sub)

#turn CHR and P0 into numerical variables 
twas_sub$CHR <- as.numeric(as.character(twas_sub$CHR))
twas_sub$P0 <- as.numeric(as.character(twas_sub$P0))
str(twas_sub)

###
# Keep only the most sign. feature from the CMC brain splicing results
###
twas_sub2 <- twas_sub[order(twas_sub$TWAS.P), ]
head(twas_sub2)
tail(twas_sub2)

library(dplyr)
twas_sub  <- twas_sub2 %>% distinct(ID, PANEL, .keep_all = T)


###
# Create df for groups of tissues
###

#order by CHR and P0 first
twas2 <- twas_sub[order(twas_sub$CHR,twas_sub$P0),]
twas_sub <- twas2


#create df
twas_brain.df <- twas_sub[twas_sub$PANEL %in% c("Brain_Amygdala", "Brain_Anterior_cingulate_cortex_BA24", "Brain_Caudate_basal_ganglia", "Brain_Cerebellar_Hemisphere", "Brain_Cerebellum", "Brain_Cortex", "Brain_Frontal_Cortex_BA9", "Brain_Hippocampus", "Brain_Hypothalamus", "Brain_Nucleus_accumbens_basal_ganglia", "Brain_Putamen_basal_ganglia", "Brain_Substantia_nigra", "CMC.BRAIN.RNASEQ", "CMC.BRAIN.RNASEQ_SPLICING","PsychENCODE"), ]
twas_blood.df <- twas_sub[twas_sub$PANEL %in% c("Whole_Blood", "NTR.BLOOD.RNAARR", "YFS.BLOOD.RNAARR"), ]
twas_HPA.df <- twas_sub[twas_sub$PANEL %in% c("Brain_Hypothalamus", "Pituitary", "Adrenal_Gland"), ]
twas_HPT.df <- twas_sub[twas_sub$PANEL %in% c("Brain_Hypothalamus", "Pituitary","Thyroid"), ]

###
#filter for gene IDs significant in a given group of tissues only 
###

#get sign. gene ID per group of tissues
sign_feat_brain<-twas_brain.df[twas_brain.df$TWAS.P < 1.368572e-06,]$ID  #to get a vector with the gene IDs of the significant features
#101 features are sign. within brain snp weight sets
str(sign_feat_brain)
sign_feat_brain <- unique(sign_feat_brain)  #111 unique genes differentially expressed in brain snp weights


sign_feat_blood<-twas_blood.df[twas_blood.df$TWAS.P < 1.368572e-06,]$ID 
str(sign_feat_blood) #26
sign_feat_blood <- unique(sign_feat_blood)#23


sign_feat_HPA<-twas_HPA.df[twas_HPA.df$TWAS.P < 1.368572e-06,]$ID  #to get a vector with the gene IDs of the significant features
str(sign_feat_HPA) #28
sign_feat_HPA <- unique(sign_feat_HPA)  #22


sign_feat_HPT<-twas_HPT.df[twas_HPT.df$TWAS.P < 1.368572e-06,]$ID  #to get a vector with the gene IDs of the significant features
str(sign_feat_HPT) #41
sign_feat_HPT <- unique(sign_feat_HPT)  #34

#filter
twas_brain.df <- twas_brain.df[(twas_brain.df$ID %in% sign_feat_brain), ]
#320 obs.
twas_blood.df <- twas_blood.df[(twas_blood.df$ID %in% sign_feat_blood), ] #32
twas_HPA.df <- twas_HPA.df[(twas_HPA.df$ID %in% sign_feat_HPA), ] #32
twas_HPT.df <- twas_HPT.df[(twas_HPT.df$ID %in% sign_feat_HPT), ] #51 features with the gene ID within the vector sign feat...

#create vectors with the z scores of the features within specific tissues
TWAS.Z.brain <- twas_brain.df$TWAS.Z
TWAS.Z.blood <- twas_blood.df$TWAS.Z
TWAS.Z.HPA <- twas_HPA.df$TWAS.Z
TWAS.Z.HPT <- twas_HPT.df$TWAS.Z

# Make ID a factor for plotting
twas_brain.df$ID<-factor(twas_brain.df$ID, levels = unique(twas_brain.df$ID))
twas_blood.df$ID<-factor(twas_blood.df$ID, levels = unique(twas_blood.df$ID))
twas_HPA.df$ID<-factor(twas_HPA.df$ID, levels = unique(twas_HPA.df$ID))
twas_HPT.df$ID<-factor(twas_HPT.df$ID, levels = unique(twas_HPT.df$ID))

#####
# Heatmap for brain SNP-weight sets
#####

twas_brain.df_unique<-twas_brain.df[!duplicated(twas_brain.df$ID),]
vline_1<-min(which(twas_brain.df_unique$CHR == 6 & twas_brain.df_unique$P0 > 26e6 & twas_brain.df_unique$P1 < 34e6))
vline_2<-max(which(twas_brain.df_unique$CHR == 6 & twas_brain.df_unique$P0 > 26e6 & twas_brain.df_unique$P1 < 34e6))

#Plot brain SNP weights
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_brain.png",width = 5100, height = 1400, units = "px", res=300)

ggplot(data = twas_brain.df, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +   
  geom_tile(aes(fill = TWAS.Z.brain), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z.brain,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in brain SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)

dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

#####
# Plot for blood SNP-weight sets
#####

twas_blood.df_unique<-twas_blood.df[!duplicated(twas_blood.df$ID),]
vline_1<-min(which(twas_blood.df_unique$CHR == 6 & twas_blood.df_unique$P0 > 26e6 & twas_blood.df_unique$P1 < 34e6))
vline_2<-max(which(twas_blood.df_unique$CHR == 6 & twas_blood.df_unique$P0 > 26e6 & twas_blood.df_unique$P1 < 34e6))

#Plot blood findings
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_blood.png",width = 2400, height = 800, units = "px", res=300)

ggplot(data = twas_blood.df, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +   
  geom_tile(aes(fill = TWAS.Z.blood), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z.blood,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in blood SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

#####
# Plot for HPA axis SNP-weight sets
#####

twas_HPA.df_unique<-twas_HPA.df[!duplicated(twas_HPA.df$ID),]
vline_1<-min(which(twas_HPA.df_unique$CHR == 6 & twas_HPA.df_unique$P0 > 26e6 & twas_HPA.df_unique$P1 < 34e6))
vline_2<-max(which(twas_HPA.df_unique$CHR == 6 & twas_HPA.df_unique$P0 > 26e6 & twas_HPA.df_unique$P1 < 34e6))

#Plot HPA axis findings
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_HPA.png",width = 2400, height = 800, units = "px", res=300)

ggplot(data = twas_HPA.df, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +   
  geom_tile(aes(fill = TWAS.Z.HPA), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z.HPA,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in HPA axis SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

#####
# Plot for HPT axis SNP-weight sets
#####

twas_HPT.df_unique<-twas_HPT.df[!duplicated(twas_HPT.df$ID),]
vline_1<-min(which(twas_HPT.df_unique$CHR == 6 & twas_HPT.df_unique$P0 > 26e6 & twas_HPT.df_unique$P1 < 34e6))
vline_2<-max(which(twas_HPT.df_unique$CHR == 6 & twas_HPT.df_unique$P0 > 26e6 & twas_HPT.df_unique$P1 < 34e6))

#Plot HPT axis findings
png("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS.TWAS_Z_heatmap_HPT.png",width = 3000, height = 800, units = "px", res=300)
ggplot(data = twas_HPT.df, aes(x = ID, y = PANEL_clean_short)) +
  theme_bw()    +   
  geom_tile(aes(fill = TWAS.Z.HPT), colour = 'black') +
  scale_fill_gradientn(colours=c("dodgerblue2","white","red"), na.value = 'white',name = "Z-score") +
  theme(axis.text.x = element_text(angle = 45, hjust = 1),plot.title = element_text(hjust = 0.5)) +
  geom_text(aes(label=round(TWAS.Z.HPT,1)), color="black", size=3) +
  labs(title="Genes differentially expressed in HPT axis SNP-weight sets",  x ="Gene ID", y = "SNP-weight sets") +
  geom_vline(xintercept = vline_1-0.5, size=1) +
  geom_vline(xintercept = vline_2+0.5, size=1)
dev.off()

# Note significant features presented a z-score > 4.83 or < -4.83.  

Show plots

MDD TWAS Heatmap Shared

MDD TWAS Heatmap Shared


MDD TWAS Heatmap Unique

MDD TWAS Heatmap Unique


MDD TWAS Heatmap Brain ***

MDD TWAS Heatmap Blood

MDD TWAS Heatmap Blood


MDD TWAS Heatmap HPA

MDD TWAS Heatmap HPA


MDD TWAS Heatmap HPT

MDD TWAS Heatmap HPT



3.5 Process colocalisation results

Colocalisation identified whether TWAS and GWAS associations result from the same or distinct causal SNP.

Organise coloc results

###
# Create a table with colocalisation results for all significant features
###
rm(list=ls())
library(data.table)
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")

library(tibble)
twas_sign <- as_data_frame(twas_sign)
colnames(twas_sign)

twas_sign$Location<-paste0('chr',twas_sign$CHR,':',twas_sign$P0,'-',twas_sign$P1)   

#transform variables into numeric and then order by them
str(twas_sign)
as.numeric(as.character(twas_sign$CHR))
as.numeric(as.character(twas_sign$P0))
twas_sign_ordered <- twas_sign[order(twas_sign$CHR, twas_sign$P0), ]

col_order <- c("Location", "ID", "PANEL_clean_short", "TWAS.Z", "TWAS.P", "COLOC.PP0", "COLOC.PP1", "COLOC.PP2", "COLOC.PP3", "COLOC.PP4")
twas_sign_ordered <- twas_sign_ordered[, col_order]
twas_sign_ordered

###
#Create a couple of additional columns specifying whether the feature is colocalised or not
###

#to specify coloc pp4 > 0.8 (see gusev et al (2019) Nat Genet on epithelial ovarian cancer)
twas_sign_ordered$High_PP4_0.8 <- NULL
twas_sign_ordered$High_PP4_0.8 <- ifelse(twas_sign_ordered$COLOC.PP4 > 0.8, "Yes", "No")
sum(twas_sign_ordered$High_PP4_0.8 == "Yes")  #97 features present a PP4 greater than 0.8

#to specify coloc pp3 < 0.2 
twas_sign_ordered$Low_PP3_0.2 <- NULL
twas_sign_ordered$Low_PP3_0.2 <- ifelse(twas_sign_ordered$COLOC.PP3 < 0.2, "Yes", "No")
sum(twas_sign_ordered$Low_PP3_0.2 == "Yes") #140 features present a PP3 smaller than 0.2

#specify whether both conditions are satisfied (NB PP4 > 0.8 is much more of a stringent threshold)
twas_sign_ordered$Colocalised <- NULL
twas_sign_ordered$Colocalised <- ifelse(twas_sign_ordered$High_PP4_0.8 == "Yes" & twas_sign_ordered$Low_PP3_0.2 == "Yes", "Yes", "No")
sum(twas_sign_ordered$Colocalised == "Yes") #97 features are colocalised

#get the number of unique genes which were colocalised
colocalised_df <- twas_sign_ordered[twas_sign_ordered$Colocalised == "Yes", ] #as expected, dim = 97, 13
colocalised_vector <- colocalised_df$ID 
unique_genes_colocalised <- unique(colocalised_vector) #57 unique genes which were colocalised


###
# Clean and Save 
###
col_order2 <- c("Location", "ID", "PANEL_clean_short", "TWAS.Z", "TWAS.P", "COLOC.PP0", "COLOC.PP1", "COLOC.PP2", "COLOC.PP3", "COLOC.PP4", "Low_PP3_0.2", "High_PP4_0.8", "Colocalised")
twas_sign_ordered <- twas_sign_ordered[, col_order2]
twas_sign_ordered

write.csv(twas_sign_ordered, "/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_colocalisation.csv", row.names = F)

q()
n

Show colocalisation table

MDD TWAS Colocalisation Results
Location ID PANEL_clean_short TWAS.Z TWAS.P COLOC.PP0 COLOC.PP1 COLOC.PP2 COLOC.PP3 COLOC.PP4 Low_PP3_0.2 High_PP4_0.8 Colocalised
chr1:8412457-8877702 RERE GTEx Whole Blood -5.095707 3.47e-07 0.000 0.001 0.000 0.006 0.993 Yes Yes Yes
chr1:8412457-8877702 RERE YFS Blood -5.310078 1.10e-07 0.000 0.001 0.000 0.006 0.993 Yes Yes Yes
chr1:8484705-8494898 RP5-1115A15.1 GTEx Thyroid -5.175240 2.28e-07 0.000 0.001 0.000 0.004 0.995 Yes Yes Yes
chr1:8484705-8494898 RP5-1115A15.1 GTEx Whole Blood -4.866386 1.14e-06 0.012 0.001 0.088 0.008 0.891 Yes Yes Yes
chr1:36884051-36884179 SNORA63 GTEx Nucleus accumbens 4.848870 1.24e-06 0.067 0.006 0.257 0.023 0.647 Yes No No
chr1:71861623-72748417 NEGR1 GTEx Caudate 5.780100 7.47e-09 0.000 0.000 0.284 0.036 0.681 Yes No No
chr1:71861623-72748417 NEGR1 GTEx Putamen 5.548510 2.88e-08 0.000 0.000 0.018 0.014 0.968 Yes Yes Yes
chr1:71861623-72748417 NEGR1 GTEx Whole Blood 8.760622 1.94e-18 0.000 0.000 0.000 0.007 0.993 Yes Yes Yes
chr1:72767155-72767512 RPL31P12 GTEx Cerebellar Hemispher… -7.785520 6.94e-15 0.000 0.000 0.000 0.010 0.990 Yes Yes Yes
chr1:72767155-72767512 RPL31P12 GTEx Cerebellum -7.708820 1.27e-14 0.000 0.000 0.000 0.006 0.994 Yes Yes Yes
chr1:72767155-72767512 RPL31P12 PsychENCODE -7.742756 9.73e-15 0.000 0.000 0.000 0.007 0.993 Yes Yes Yes
chr1:175873898-175889649 RP11-318C24.2 GTEx Thyroid -5.027510 4.97e-07 0.004 0.007 0.012 0.018 0.959 Yes Yes Yes
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing -4.958690 7.10e-07 0.000 0.008 0.000 0.021 0.971 Yes Yes Yes
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing 5.039850 4.66e-07 0.000 0.007 0.000 0.020 0.973 Yes Yes Yes
chr1:175913966-176176370 RFWD2 CMC DLPFC Splicing -5.005960 5.56e-07 0.000 0.007 0.000 0.019 0.973 Yes Yes Yes
chr1:181452685-181775921 CACNA1E CMC DLPFC Splicing -4.989390 6.06e-07 0.000 0.151 0.000 0.420 0.429 No No No
chr1:197473878-197744623 DENND1B CMC DLPFC 4.848374 1.24e-06 0.000 0.001 0.000 0.011 0.988 Yes Yes Yes
chr1:197473878-197744623 DENND1B CMC DLPFC Splicing -5.421950 5.90e-08 0.000 0.001 0.000 0.010 0.989 Yes Yes Yes
chr1:197473878-197744623 DENND1B CMC DLPFC Splicing 5.018050 5.22e-07 0.000 0.001 0.000 0.010 0.989 Yes Yes Yes
chr2:58386377-58468515 FANCL CMC DLPFC -5.183180 2.18e-07 0.001 0.000 0.056 0.027 0.916 Yes Yes Yes
chr2:58386377-58468515 FANCL CMC DLPFC Splicing 4.897476 9.71e-07 0.000 0.001 0.007 0.104 0.888 Yes Yes Yes
chr2:197831741-198175897 ANKRD44 YFS Blood -5.690140 1.27e-08 0.062 0.023 0.168 0.061 0.686 Yes No No
chr2:198254508-198299815 SF3B1 GTEx Hypothalamus 5.214900 1.84e-07 0.071 0.015 0.319 0.068 0.526 Yes No No
chr3:44481261-44561226 ZNF445 CMC DLPFC -5.103280 3.34e-07 0.000 0.087 0.002 0.601 0.310 No No No
chr4:41937137-41962589 TMEM33 PsychENCODE 4.837418 1.32e-06 0.000 0.001 0.000 0.074 0.925 Yes Yes Yes
chr4:41983713-41988476 DCAF4L1 GTEx Thyroid -5.128000 2.93e-07 0.003 0.001 0.291 0.091 0.615 Yes No No
chr4:41990758-41991254 RP11-814H16.2 GTEx Cerebellar Hemispher… 5.009600 5.45e-07 0.005 0.000 0.561 0.055 0.378 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx Amygdala -5.253400 1.49e-07 0.004 0.001 0.476 0.070 0.450 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx ACC -5.001690 5.68e-07 0.003 0.001 0.399 0.101 0.496 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx Caudate -4.854800 1.21e-06 0.003 0.001 0.388 0.086 0.521 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx Cortex -5.774530 7.72e-09 0.003 0.001 0.340 0.113 0.543 Yes No No
chr4:41992489-42092474 SLC30A9 GTEx Hypothalamus -5.085140 3.67e-07 0.000 0.000 0.016 0.039 0.944 Yes Yes Yes
chr4:41992489-42092474 SLC30A9 GTEx Nucleus accumbens -5.602700 2.11e-08 0.001 0.001 0.072 0.119 0.808 Yes Yes Yes
chr4:41992489-42092474 SLC30A9 PsychENCODE -5.259200 1.45e-07 0.000 0.000 0.000 0.026 0.974 Yes Yes Yes
chr5:87564712-87732502 TMEM161B-AS1 PsychENCODE 6.091010 1.12e-09 0.000 0.000 0.000 0.117 0.883 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Adrenal Gland 5.360090 8.32e-08 0.000 0.000 0.000 0.086 0.914 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Amygdala 6.118500 9.45e-10 0.000 0.000 0.004 0.058 0.938 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx ACC 6.445500 1.15e-10 0.000 0.000 0.000 0.056 0.944 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Caudate 6.282167 3.34e-10 0.000 0.000 0.000 0.062 0.938 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cerebellar Hemispher… 6.011700 1.84e-09 0.000 0.000 0.000 0.062 0.938 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cerebellum 6.053050 1.42e-09 0.000 0.000 0.000 0.052 0.948 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Cortex 6.021420 1.73e-09 0.000 0.000 0.000 0.070 0.930 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Frontal Cortex 6.720000 1.82e-11 0.000 0.000 0.000 0.086 0.914 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Hypothalamus 5.875800 4.21e-09 0.000 0.000 0.000 0.060 0.940 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Nucleus accumbens 6.010490 1.85e-09 0.000 0.000 0.000 0.059 0.941 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Putamen 6.372050 1.87e-10 0.000 0.000 0.000 0.054 0.946 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Substantia nigra 6.057270 1.38e-09 0.000 0.000 0.009 0.054 0.937 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Pituitary 6.048500 1.46e-09 0.000 0.000 0.000 0.050 0.950 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Thyroid 5.889760 3.87e-09 0.000 0.000 0.000 0.079 0.920 Yes Yes Yes
chr5:87564888-87732502 TMEM161B-AS1 GTEx Whole Blood 5.526440 3.27e-08 0.000 0.000 0.000 0.048 0.952 Yes Yes Yes
chr5:87729709-87794514 CTC-498M16.4 GTEx Substantia nigra 5.403610 6.53e-08 0.000 0.000 0.391 0.037 0.572 Yes No No
chr5:87988462-87989789 CTC-467M3.3 GTEx ACC -5.813700 6.11e-09 0.000 0.000 0.631 0.053 0.317 Yes No No
chr5:87988462-87989789 CTC-467M3.3 GTEx Cerebellar Hemispher… -5.861000 4.60e-09 0.000 0.000 0.056 0.051 0.893 Yes Yes Yes
chr5:87988462-87989789 CTC-467M3.3 GTEx Cortex -6.510990 7.47e-11 0.000 0.000 0.091 0.040 0.870 Yes Yes Yes
chr5:87988462-87989789 CTC-467M3.3 GTEx Frontal Cortex -7.091600 1.33e-12 0.000 0.000 0.114 0.035 0.850 Yes Yes Yes
chr5:87988462-87989789 CTC-467M3.3 PsychENCODE -6.097890 1.07e-09 0.000 0.000 0.035 0.251 0.715 No No No
chr5:140024947-140027370 NDUFA2 CMC DLPFC 5.190020 2.10e-07 0.104 0.037 0.170 0.060 0.629 Yes No No
chr5:140201222-140203811 PCDHA5 GTEx Thyroid -5.402970 6.55e-08 0.055 0.028 0.090 0.046 0.781 Yes No No
chr5:140220907-140223351 PCDHA8 GTEx Cerebellar Hemispher… -4.980100 6.36e-07 0.001 0.032 0.001 0.051 0.915 Yes Yes Yes
chr6:26188921-26189323 HIST1H4D NTR Blood -4.987600 6.11e-07 0.000 0.000 0.309 0.059 0.632 Yes No No
chr6:26365386-26378540 BTN3A2 NTR Blood 5.326600 1.00e-07 0.000 0.000 0.000 0.562 0.438 No No No
chr6:26365387-26378546 BTN3A2 GTEx Cerebellar Hemispher… 5.188200 2.12e-07 0.000 0.000 0.000 0.505 0.495 No No No
chr6:26365387-26378546 BTN3A2 GTEx Hippocampus 4.963000 6.96e-07 0.000 0.000 0.000 0.506 0.494 No No No
chr6:26365387-26378546 BTN3A2 GTEx Pituitary 5.898930 3.66e-09 0.000 0.000 0.000 0.501 0.499 No No No
chr6:26365387-26378546 BTN3A2 GTEx Thyroid 5.481600 4.22e-08 0.000 0.000 0.000 0.507 0.492 No No No
chr6:26365387-26378546 BTN3A2 GTEx Whole Blood 5.086960 3.64e-07 0.000 0.000 0.000 0.543 0.457 No No No
chr6:26538633-26546482 HMGN4 GTEx Cerebellum 5.395400 6.84e-08 0.000 0.000 0.404 0.299 0.297 No No No
chr6:27215480-27224250 PRSS16 GTEx Cerebellar Hemispher… -4.891200 1.00e-06 0.000 0.000 0.096 0.322 0.582 No No No
chr6:27215480-27224250 PRSS16 GTEx Cerebellum -4.947900 7.50e-07 0.000 0.001 0.000 0.962 0.037 No No No
chr6:27215480-27224250 PRSS16 GTEx Frontal Cortex -5.045000 4.54e-07 0.000 0.000 0.366 0.056 0.577 Yes No No
chr6:27215480-27224250 PRSS16 GTEx Pituitary -5.916080 3.30e-09 0.000 0.000 0.044 0.035 0.921 Yes Yes Yes
chr6:27215480-27224250 PRSS16 GTEx Whole Blood -5.335920 9.51e-08 0.000 0.001 0.047 0.474 0.479 No No No
chr6:27325604-27339304 ZNF204P GTEx Adrenal Gland -5.032700 4.84e-07 0.000 0.000 0.547 0.211 0.242 No No No
chr6:27371789-27374743 RP1-153G14.4 GTEx Hippocampus 5.354000 8.60e-08 0.000 0.000 0.676 0.142 0.182 Yes No No
chr6:27418522-27440897 ZNF184 GTEx Caudate -6.325200 2.53e-10 0.000 0.000 0.231 0.114 0.655 Yes No No
chr6:27418522-27440897 ZNF184 GTEx Hypothalamus -4.952200 7.34e-07 0.000 0.000 0.174 0.185 0.641 Yes No No
chr6:27840926-27841289 HIST1H4L NTR Blood 4.870800 1.11e-06 0.000 0.000 0.001 0.162 0.838 Yes Yes Yes
chr6:28058932-28061442 ZSCAN12P1 PsychENCODE 6.268010 3.66e-10 0.000 0.000 0.056 0.626 0.318 No No No
chr6:28058932-28061442 ZSCAN12P1 GTEx Whole Blood -4.936930 7.94e-07 0.000 0.000 0.388 0.365 0.247 No No No
chr6:28083406-28084329 RP1-265C24.5 GTEx Hippocampus 5.532000 3.16e-08 0.000 0.000 0.009 0.143 0.847 Yes Yes Yes
chr6:28092338-28097860 ZSCAN16 YFS Blood -6.109000 1.00e-09 0.000 0.000 0.013 0.054 0.933 Yes Yes Yes
chr6:28192664-28201260 ZSCAN9 GTEx Cerebellum -5.307800 1.11e-07 0.000 0.000 0.238 0.147 0.615 Yes No No
chr6:28192664-28201260 ZSCAN9 GTEx Hippocampus -6.017000 1.77e-09 0.000 0.000 0.080 0.053 0.866 Yes Yes Yes
chr6:28192664-28201260 ZSCAN9 GTEx Pituitary -6.159020 7.32e-10 0.000 0.000 0.169 0.191 0.640 Yes No No
chr6:28227098-28228736 NKAPL PsychENCODE 5.002860 5.65e-07 0.000 0.000 0.627 0.332 0.041 No No No
chr6:28234788-28245974 RP5-874C20.3 GTEx Adrenal Gland 5.094600 3.49e-07 0.000 0.000 0.031 0.146 0.822 Yes Yes Yes
chr6:28234788-28245974 RP5-874C20.3 GTEx Cerebellum 5.062800 4.13e-07 0.000 0.000 0.000 0.756 0.244 No No No
chr6:28234788-28245974 RP5-874C20.3 GTEx Hippocampus 5.198000 2.01e-07 0.000 0.000 0.172 0.174 0.654 Yes No No
chr6:28234788-28245974 RP5-874C20.3 GTEx Putamen 5.739000 9.52e-09 0.000 0.000 0.018 0.051 0.931 Yes Yes Yes
chr6:28234788-28245974 RP5-874C20.3 GTEx Thyroid 5.338400 9.38e-08 0.000 0.000 0.000 0.858 0.141 No No No
chr6:28234788-28245974 RP5-874C20.3 GTEx Whole Blood 5.662330 1.49e-08 0.000 0.000 0.288 0.107 0.604 Yes No No
chr6:28249314-28270326 PGBD1 GTEx Cerebellar Hemispher… -6.313100 2.74e-10 0.000 0.000 0.032 0.017 0.950 Yes Yes Yes
chr6:28292470-28324048 ZSCAN31 GTEx Amygdala -5.084150 3.69e-07 0.000 0.000 0.381 0.404 0.214 No No No
chr6:28317691-28336947 ZKSCAN3 GTEx Amygdala 4.949900 7.43e-07 0.000 0.000 0.777 0.111 0.111 Yes No No
chr6:28317691-28336947 ZKSCAN3 GTEx Hippocampus 4.951000 7.37e-07 0.000 0.000 0.389 0.273 0.338 No No No
chr6:28317691-28336947 ZKSCAN3 GTEx Thyroid 6.093300 1.11e-09 0.000 0.000 0.000 0.086 0.914 Yes Yes Yes
chr6:28399707-28411279 ZSCAN23 GTEx Hypothalamus -5.777500 7.58e-09 0.000 0.000 0.062 0.179 0.758 Yes No No
chr6:28399707-28411279 ZSCAN23 GTEx Putamen -4.894000 9.90e-07 0.000 0.000 0.239 0.226 0.535 No No No
chr6:28399707-28411279 ZSCAN23 GTEx Pituitary -4.953290 7.30e-07 0.000 0.000 0.002 0.275 0.723 No No No
chr6:30644166-30655672 PPP1R18 GTEx Adrenal Gland 4.910200 9.10e-07 0.106 0.007 0.130 0.007 0.750 Yes No No
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.299700 1.16e-07 0.000 0.001 0.000 0.000 0.999 Yes Yes Yes
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing -5.067100 4.04e-07 0.000 0.001 0.000 0.000 0.999 Yes Yes Yes
chr6:30695485-30710682 FLOT1 CMC DLPFC Splicing 4.936600 7.95e-07 0.000 0.001 0.000 0.000 0.999 Yes Yes Yes
chr6:30695486-30710510 FLOT1 GTEx Cerebellum -5.299000 1.16e-07 0.010 0.001 0.012 0.000 0.976 Yes Yes Yes
chr6:30695486-30710510 FLOT1 GTEx Pituitary -5.253270 1.49e-07 0.016 0.001 0.020 0.000 0.963 Yes Yes Yes
chr6:30695486-30710510 FLOT1 GTEx Thyroid -5.557400 2.74e-08 0.000 0.001 0.000 0.000 0.999 Yes Yes Yes
chr6:30881982-30894236 VARS2 GTEx Cortex 5.922000 3.18e-09 0.105 0.004 0.158 0.005 0.727 Yes No No
chr6:30881982-30894236 VARS2 GTEx Whole Blood 6.323130 2.56e-10 0.005 0.001 0.007 0.000 0.986 Yes Yes Yes
chr6:31255287-31256741 WASF5P GTEx Pituitary -5.156240 2.52e-07 0.000 0.046 0.000 0.091 0.862 Yes Yes Yes
chr6:31368479-31445283 HCP5 GTEx Thyroid 6.400800 1.55e-10 0.000 0.008 0.000 0.015 0.976 Yes Yes Yes
chr6:31462658-31478901 MICB GTEx Thyroid -5.557000 2.74e-08 0.000 0.047 0.000 0.094 0.859 Yes Yes Yes
chr6:31606805-31620482 BAG6 CMC DLPFC Splicing -5.580000 2.40e-08 0.008 0.408 0.006 0.319 0.259 No No No
chr6:31694815-31698357 DDAH2 GTEx Frontal Cortex 5.409500 6.32e-08 0.331 0.042 0.258 0.033 0.336 Yes No No
chr6:31694816-31698039 DDAH2 CMC DLPFC 5.344500 9.07e-08 0.000 0.051 0.000 0.039 0.909 Yes Yes Yes
chr6:99817347-99842082 COQ3 CMC DLPFC Splicing 5.146560 2.65e-07 0.324 0.015 0.598 0.029 0.034 Yes No No
chr6:105404922-105531207 LIN28B CMC DLPFC -5.232050 1.68e-07 0.000 0.001 0.000 0.008 0.990 Yes Yes Yes
chr6:105404923-105531207 LIN28B PsychENCODE -5.105689 3.30e-07 0.000 0.005 0.000 0.052 0.943 Yes Yes Yes
chr6:105584224-105617820 BVES-AS1 GTEx Amygdala -5.578300 2.43e-08 0.061 0.007 0.373 0.045 0.514 Yes No No
chr7:12250867-12282993 TMEM106B GTEx Adrenal Gland 5.505026 3.69e-08 0.000 0.001 0.003 0.009 0.987 Yes Yes Yes
chr7:12250867-12282993 TMEM106B PsychENCODE -5.790690 7.01e-09 0.000 0.001 0.000 0.054 0.945 Yes Yes Yes
chr7:12250867-12282993 TMEM106B GTEx Whole Blood 5.531000 3.18e-08 0.000 0.001 0.000 0.008 0.991 Yes Yes Yes
chr7:12250867-12276886 TMEM106B YFS Blood 5.373600 7.72e-08 0.000 0.001 0.000 0.007 0.993 Yes Yes Yes
chr7:24836158-25021253 OSBPL3 GTEx Pituitary -5.622890 1.88e-08 0.090 0.040 0.062 0.027 0.780 Yes No No
chr8:52232136-52722005 PXDNL CMC DLPFC 5.887460 3.92e-09 0.090 0.019 0.318 0.065 0.508 Yes No No
chr8:61297147-61429354 RP11-163N6.2 GTEx Thyroid -5.336530 9.47e-08 0.084 0.162 0.118 0.228 0.408 No No No
chr9:126605315-126605965 PIGFP2 PsychENCODE -5.305600 1.12e-07 0.017 0.004 0.603 0.126 0.250 Yes No No
chr11:57067112-57092426 TNKS1BP1 GTEx Adrenal Gland 4.922610 8.54e-07 0.080 0.025 0.107 0.032 0.756 Yes No No
chr11:57405497-57420263 AP000662.4 GTEx Thyroid -4.980256 6.35e-07 0.000 0.136 0.000 0.255 0.610 No No No
chr11:57424488-57429340 CLP1 GTEx Whole Blood 5.195860 2.04e-07 0.001 0.008 0.002 0.015 0.974 Yes Yes Yes
chr11:61535973-61560274 TMEM258 PsychENCODE 5.021730 5.12e-07 0.000 0.049 0.000 0.041 0.910 Yes Yes Yes
chr11:113280318-113346111 DRD2 GTEx Frontal Cortex -5.073787 3.90e-07 0.366 0.032 0.515 0.045 0.042 Yes No No
chr13:53602875-53626196 OLFM4 CMC DLPFC 5.091290 3.56e-07 0.000 0.000 0.865 0.089 0.046 Yes No No
chr14:42057064-42074059 CTD-2298J14.2 GTEx Thyroid -5.678860 1.36e-08 0.000 0.000 0.000 0.022 0.978 Yes Yes Yes
chr14:42076773-42373752 LRFN5 GTEx Cerebellar Hemispher… 5.423400 5.85e-08 0.000 0.000 0.000 0.029 0.971 Yes Yes Yes
chr14:42076773-42373752 LRFN5 GTEx Cerebellum 5.597540 2.17e-08 0.000 0.000 0.000 0.041 0.959 Yes Yes Yes
chr14:59951161-59971429 JKAMP GTEx Thyroid -5.125100 2.97e-07 0.001 0.004 0.004 0.022 0.969 Yes Yes Yes
chr14:59971779-60043549 CCDC175 GTEx Thyroid -5.478850 4.28e-08 0.000 0.004 0.000 0.018 0.979 Yes Yes Yes
chr14:60062693-60337557 RTN1 CMC DLPFC Splicing -4.874920 1.09e-06 0.001 0.006 0.007 0.033 0.953 Yes Yes Yes
chr14:60062695-60337684 RTN1 GTEx Thyroid -5.348450 8.87e-08 0.000 0.003 0.000 0.016 0.981 Yes Yes Yes
chr14:64319682-64693151 SYNE2 NTR Blood 5.609528 2.03e-08 0.000 0.000 0.000 0.016 0.984 Yes Yes Yes
chr14:64550950-64770377 ESR2 GTEx Pituitary -5.982300 2.20e-09 0.000 0.000 0.113 0.026 0.860 Yes Yes Yes
chr14:64550950-64770377 ESR2 GTEx Whole Blood -5.655371 1.56e-08 0.000 0.000 0.000 0.014 0.986 Yes Yes Yes
chr14:75120140-75179818 AREL1 PsychENCODE -5.015110 5.30e-07 0.000 0.002 0.000 0.216 0.782 No No No
chr14:75319736-75330537 PROX2 GTEx Thyroid -5.758100 8.51e-09 0.000 0.000 0.017 0.020 0.962 Yes Yes Yes
chr14:75348593-75370450 DLST CMC DLPFC 4.981400 6.31e-07 0.000 0.001 0.000 0.047 0.952 Yes Yes Yes
chr14:75348594-75370448 DLST PsychENCODE 5.089700 3.59e-07 0.000 0.000 0.000 0.023 0.977 Yes Yes Yes
chr14:75370656-75389188 RPS6KL1 CMC DLPFC Splicing -5.023810 5.07e-07 0.003 0.001 0.205 0.082 0.708 Yes No No
chr14:75370657-75390099 RPS6KL1 PsychENCODE -4.952550 7.32e-07 0.002 0.000 0.176 0.031 0.791 Yes No No
chr14:103878456-103879098 RP11-600F24.2 PsychENCODE 5.185660 2.15e-07 0.007 0.002 0.552 0.202 0.238 No No No
chr14:103985996-103989448 CKB YFS Blood 5.346000 8.99e-08 0.000 0.001 0.000 0.005 0.995 Yes Yes Yes
chr14:103995508-104003410 TRMT61A CMC DLPFC 5.051300 4.39e-07 0.001 0.004 0.004 0.030 0.961 Yes Yes Yes
chr14:103995521-104003410 TRMT61A GTEx Whole Blood 4.977593 6.44e-07 0.006 0.010 0.049 0.081 0.854 Yes Yes Yes
chr14:104019758-104028214 RP11-894P9.2 GTEx Thyroid -5.462560 4.69e-08 0.000 0.001 0.000 0.005 0.994 Yes Yes Yes
chr14:104153913-104154464 RP11-73M18.6 PsychENCODE 5.031320 4.87e-07 0.000 0.005 0.001 0.413 0.581 No No No
chr14:104160897-104161507 RP11-73M18.7 PsychENCODE 4.856130 1.20e-06 0.000 0.006 0.000 0.513 0.480 No No No
chr14:104162690-104163500 RP11-73M18.8 GTEx Amygdala 5.142000 2.72e-07 0.010 0.002 0.082 0.019 0.887 Yes Yes Yes
chr14:104177607-104179149 AL049840.1 GTEx Cerebellum 5.029540 4.92e-07 0.001 0.003 0.008 0.026 0.962 Yes Yes Yes
chr14:104177607-104179149 AL049840.1 GTEx Cortex 5.143620 2.69e-07 0.001 0.002 0.007 0.012 0.979 Yes Yes Yes
chr14:104179904-104180441 RP11-73M18.9 GTEx Cortex 4.977330 6.45e-07 0.000 0.002 0.001 0.013 0.984 Yes Yes Yes
chr14:104179904-104180586 RP11-73M18.9 PsychENCODE 4.830100 1.36e-06 0.001 0.005 0.049 0.425 0.520 No No No
chr16:72146056-72210777 PMFBP1 PsychENCODE -5.160620 2.46e-07 0.013 0.005 0.174 0.069 0.738 Yes No No
chr17:27400528-27507430 MYO18A GTEx Adrenal Gland -5.128570 2.92e-07 0.002 0.040 0.001 0.019 0.937 Yes Yes Yes
chr17:27401933-27405875 TIAF1 GTEx Adrenal Gland -5.361200 8.27e-08 0.016 0.111 0.008 0.055 0.810 Yes Yes Yes
chr17:65520597-65521538 CTD-2653B5.1 PsychENCODE 5.105730 3.30e-07 0.000 0.441 0.000 0.028 0.531 Yes No No
chr18:52385091-52562747 RAB27B PsychENCODE 5.012900 5.36e-07 0.000 0.015 0.000 0.328 0.657 No No No
chr18:52495707-52562747 RAB27B CMC DLPFC Splicing 4.843190 1.28e-06 0.000 0.016 0.000 0.038 0.945 Yes Yes Yes
chr20:47835831-47860614 DDX27 CMC DLPFC 4.836260 1.32e-06 0.003 0.067 0.001 0.029 0.900 Yes Yes Yes
chr22:41165634-41215403 SLC25A17 GTEx Nucleus accumbens 5.076990 3.83e-07 0.007 0.001 0.547 0.097 0.348 Yes No No
chr22:41165634-41215403 SLC25A17 GTEx Thyroid 4.896100 9.78e-07 0.000 0.008 0.020 0.671 0.301 No No No
chr22:41253088-41351450 XPNPEP3 GTEx Frontal Cortex 4.951000 7.38e-07 0.009 0.001 0.731 0.091 0.168 Yes No No
chr22:41258260-41363888 XPNPEP3 CMC DLPFC 5.110000 3.21e-07 0.000 0.004 0.005 0.358 0.632 No No No
chr22:41487790-41576081 EP300 GTEx Cerebellum 5.493900 3.93e-08 0.001 0.001 0.061 0.049 0.888 Yes Yes Yes
chr22:41487790-41576081 EP300 YFS Blood 5.059100 4.21e-07 0.000 0.012 0.000 0.955 0.033 No No No
chr22:41641614-41682216 RANGAP1 CMC DLPFC Splicing 5.240100 1.61e-07 0.010 0.000 0.814 0.028 0.147 Yes No No
chr22:41641615-41682255 RANGAP1 PsychENCODE -5.575273 2.47e-08 0.000 0.004 0.000 0.705 0.290 No No No
chr22:41697526-41756151 ZC3H7B GTEx Cerebellum 5.729100 1.01e-08 0.001 0.000 0.105 0.031 0.862 Yes Yes Yes


140 of the 176 significant features presented a low posterior probability of TWAS and GWAS associations resulting from distinct causal SNPs. This is a good index of colocalisation, but PP4, should also be considered due to the possibility of other models besides PP3 and PP4 to be the most probable. When considering features with high PP4 (> 0.8), 97 of the 140 features with low PP3 also presented a high probability of GWAS and TWAS associations resulting from the same causal SNP. Therefore, based on both criteria applied, we considered 97 features as colocalised.


3.6 Process conditional analysis results

Organise coloc results

# Read in the report files
library(data.table)
setwd("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional")

# Read in the clean TWAS results
twas_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")
twas_sign$PANEL_clean<-gsub(' $','',twas_sign$PANEL_clean)

# Read in all jointly significant associations
temp = list.files(pattern=glob2rx("*chr*.report"))
report<-do.call(rbind, lapply(temp, function(x) read.table(x, header=T,stringsAsFactors=F)))
report$JOINT.ID<-NA
report$MARGIN.ID<-NA
report$JOINT.N<-NA
report$MARGIN.N<-NA
report$loc<-gsub('.*loc_','',report$FILE)
joint_res<-NULL
margin_res<-NULL

# Insert names of jointly significant genes
for(i in unique(report$CHR)){
  joint_i<-read.table(paste0('test.cond.chr',i,'.joint_included.dat'), header=T,stringsAsFactors=F)
  margin_i<-read.table(paste0('test.cond.chr',i,'.joint_dropped.dat'), header=T,stringsAsFactors=F)
  
  joint_i$path<-gsub('/[^/]+$','',joint_i$FILE)
    joint_i$path<-gsub('/[^/]+$','',joint_i$path)
    joint_i$WGT<-NA
    for(j in 1:dim(joint_i)[1]){
      joint_i$WGT[j]<-gsub(paste0(joint_i$path[j],'/'),'',joint_i$FILE[j])
  }

  if(dim(margin_i)[1] > 0){
    margin_i$path<-gsub('/[^/]+$','',margin_i$FILE)
    margin_i$path<-gsub('/[^/]+$','',margin_i$path)
    margin_i$WGT<-NA
    for(j in 1:dim(margin_i)[1]){
      margin_i$WGT[j]<-gsub(paste0(margin_i$path[j],'/'),'',margin_i$FILE[j])
    }
  }

  temp = list.files(pattern=glob2rx(paste0("*chr",i,".loc*.genes")))

  for(k in 1:length(temp)){
    loc_k<-read.table(paste0('test.cond.chr',i,'.loc_',k,'.genes'), header=T, stringsAsFactors=F)
    
    loc_k$path<-gsub('/[^/]+$','',loc_k$FILE)
    loc_k$path<-gsub('/[^/]+$','',loc_k$path)
    loc_k$WGT<-NA
    for(j in 1:dim(loc_k)[1]){
      loc_k$WGT[j]<-gsub(paste0(loc_k$path[j],'/'),'',loc_k$FILE[j])
    }

    loc_k$P0<-NULL
    loc_k$P1<-NULL
    
    loc_k<-merge(loc_k, twas_sign[,c('P0','P1','WGT','PANEL_clean')], by='WGT')
    
    loc_k_joint<-loc_k[(loc_k$WGT %in% joint_i$WGT),]
    joint_res<-rbind(joint_res,loc_k_joint)
        
    if(dim(margin_i)[1] > 0){
      loc_k_margin<-loc_k[(loc_k$WGT %in% margin_i$WGT),]
      margin_res<-rbind(margin_res,loc_k_margin)
    }
    
    g_list<-NULL
    for(g in unique(loc_k_joint$ID)){
      g_list<-c(g_list,paste0(g, " (",paste(loc_k_joint$PANEL_clean[loc_k_joint$ID == g], collapse=', '),")"))
    }
    report[report$CHR == i & report$loc == k,]$JOINT.ID<-paste(g_list,collapse=', ')

    if(dim(loc_k_margin)[1] > 0){
      g_list<-NULL
      for(g in unique(loc_k_margin$ID)){
        g_list<-c(g_list,paste0(g, " (",paste(unique(loc_k_margin$PANEL_clean[loc_k_margin$ID == g]), collapse=', '),")"))
      }
      report[report$CHR == i & report$loc == k,]$MARGIN.ID<-paste(g_list,collapse=', ')
    } else {
      report[report$CHR == i & report$loc == k,]$MARGIN.ID<-'-'
    }
    
    report[report$CHR == i & report$loc == k,]$JOINT.N<-dim(loc_k_joint)[1]
    report[report$CHR == i & report$loc == k,]$MARGIN.N<-dim(loc_k_margin)[1]
  }
}

report$LOCUS<-paste0(report$CHR,':',report$P0,':',report$P1)
report$BP<-paste0(report$P0,'-',report$P1)
report$VAR.EXP<-paste0(report$VAR.EXP*100,'%')

report<-report[,c('CHR','P0','P1','BP','LOCUS',"JOINT.N",'MARGIN.N','BEST.TWAS.P','BEST.SNP.P','VAR.EXP','JOINT.ID','MARGIN.ID')]

report<-report[order(report$CHR, report$P0),]

# Save full conditional results table
write.csv(report[,c("CHR","BP","JOINT.ID","MARGIN.ID","BEST.TWAS.P","BEST.SNP.P","VAR.EXP")],'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_full.csv', row.names=F, quote=T)

# Save brief conditional results table
write.csv(report[,c('CHR','BP','JOINT.ID','MARGIN.N','BEST.TWAS.P','BEST.SNP.P','VAR.EXP')],'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_brief.csv', row.names=F, quote=T)

# Combine gene results for marginal and joint genes
joint_res$Type<-'Joint'
margin_res$Type<-'Marginal'

gene_res<-rbind(joint_res, margin_res)

# Check number of indepenent associations
dim(joint_res) # 50

# Check number of independent associations without genome-wide significant snp
dim(joint_res[2*pnorm(-abs(joint_res$BEST.GWAS.Z)) > 5e-8,]) # 25

# Check number of independent associations with genome-wide significant snp but an r2 with predicted expression <0.1
dim(joint_res[2*pnorm(-abs(joint_res$BEST.GWAS.Z)) < 5e-8 & joint_res$TOP.SNP.COR^2 < 0.1,]) # 2

# Check number of independent novel associations
dim(joint_res[(2*pnorm(-abs(joint_res$BEST.GWAS.Z)) < 5e-8 & joint_res$TOP.SNP.COR^2 < 0.1) | 2*pnorm(-abs(joint_res$BEST.GWAS.Z)) > 5e-8,]) # 27

# Check number of novel associations
dim(gene_res[(2*pnorm(-abs(gene_res$BEST.GWAS.Z)) < 5e-8 & gene_res$TOP.SNP.COR^2 < 0.1) | 2*pnorm(-abs(gene_res$BEST.GWAS.Z)) > 5e-8,]) # 68

gene_res$Novel<-'No'
gene_res$Novel[(2*pnorm(-abs(gene_res$BEST.GWAS.Z)) < 5e-8 & gene_res$TOP.SNP.COR^2 < 0.1) | 2*pnorm(-abs(gene_res$BEST.GWAS.Z)) > 5e-8]<-'Yes'

gene_res$BP<-paste0(gene_res$P0,'-',gene_res$P1)
gene_res$BEST.GWAS.P<-2*pnorm(-abs(gene_res$BEST.GWAS.Z))

gene_res<-gene_res[order(gene_res$CHR, gene_res$P0),]

gene_res$Colocalised<-F
gene_res$Colocalised[gene_res$COLOC.PP4 >0.8]<-T

# Check number of independent novel associations which colocalise for joint genes
joint_res$Colocalised<-F
joint_res$Colocalised[joint_res$COLOC.PP4 >0.8]<-T

dim(joint_res[(2*pnorm(-abs(joint_res$BEST.GWAS.Z)) < 5e-8 & joint_res$TOP.SNP.COR^2 < 0.1 & joint_res$Colocalised == T) | (2*pnorm(-abs(joint_res$BEST.GWAS.Z)) > 5e-8 & joint_res$Colocalised == T),]) # 12

# Check number of novel associations which colocalise
dim(gene_res[(2*pnorm(-abs(gene_res$BEST.GWAS.Z)) < 5e-8 & gene_res$TOP.SNP.COR^2 < 0.1 & gene_res$Colocalised == T) | (2*pnorm(-abs(gene_res$BEST.GWAS.Z)) > 5e-8 & gene_res$Colocalised == T),]) # 45

gene_res<-gene_res[,c('CHR','BP','ID','PANEL_clean','WGT','TWAS.P','BEST.GWAS.P','TOP.SNP.COR','Type','Novel','COLOC.PP3','COLOC.PP4','Colocalised')]

# Save table showing whether gene associations are novel
write.csv(gene_res,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_novelty.csv', row.names=F, quote=T)

Show full conditional analysis table

MDD TWAS Full Conditional Results
CHR BP Jointly sign. Features (SNP-weight set) Marginally sign. Features (SNP-weight set) Top TWAS p-value Top GWAS p-value Variance Explained
1 7413452-9875347 RERE (YFS Blood) RP5-1115A15.1 (GTEx Thyroid, GTEx Whole Blood), RERE (GTEx Whole Blood) 1.10e-07 3.18e-08 100%
1 35885799-37876701 SNORA63 (GTEx Nucleus accumbens) - 1.24e-06 6.27e-08 69.3%
1 71753372-73766162 NEGR1 (GTEx Whole Blood) NEGR1 (GTEx Caudate, GTEx Putamen), RPL31P12 (GTEx Cerebellar Hemisphere, GTEx Cerebellum, PsychENCODE) 1.94e-18 4.54e-15 97.8%
1 174891875-177103690 RFWD2 (CMC DLPFC Splicing) RFWD2 (CMC DLPFC Splicing), RP11-318C24.2 (GTEx Thyroid) 4.66e-07 2.30e-07 90%
1 180725304-182724241 CACNA1E (CMC DLPFC Splicing) - 6.06e-07 1.08e-07 64%
1 196478918-198741422 DENND1B (CMC DLPFC Splicing) DENND1B (CMC DLPFC Splicing, CMC DLPFC) 5.90e-08 3.11e-08 92.6%
2 57388379-59467945 FANCL (CMC DLPFC Splicing, CMC DLPFC) - 2.18e-07 4.68e-09 85%
2 196832647-199295649 ANKRD44 (YFS Blood) SF3B1 (GTEx Hypothalamus) 1.27e-08 3.52e-07 82.8%
3 43487406-45561063 ZNF445 (CMC DLPFC) EP300 (GTEx Cerebellum, YFS Blood), XPNPEP3 (GTEx Frontal Cortex, CMC DLPFC), SLC25A17 (GTEx Nucleus accumbens, GTEx Thyroid), RANGAP1 (CMC DLPFC Splicing, PsychENCODE) 3.34e-07 6.34e-08 74.7%
4 40937584-43090938 SLC30A9 (GTEx Cortex), TMEM33 (PsychENCODE) SLC30A9 (GTEx Amygdala, GTEx ACC, GTEx Caudate, GTEx Hypothalamus, GTEx Nucleus accumbens, PsychENCODE), RP11-814H16.2 (GTEx Cerebellar Hemisphere), DCAF4L1 (GTEx Thyroid) 7.72e-09 3.59e-09 92.3%
5 86565927-88989352 TMEM161B-AS1 (GTEx Caudate, PsychENCODE), CTC-467M3.3 (GTEx Frontal Cortex), CTC-498M16.4 (GTEx Substantia nigra) TMEM161B-AS1 (GTEx Adrenal Gland, GTEx Amygdala, GTEx ACC, GTEx Cerebellar Hemisphere, GTEx Cerebellum, GTEx Cortex, GTEx Frontal Cortex, GTEx Hypothalamus, GTEx Nucleus accumbens, GTEx Putamen, GTEx Substantia nigra, GTEx Pituitary, GTEx Thyroid, GTEx Whole Blood), CTC-467M3.3 (GTEx ACC, GTEx Cerebellar Hemisphere, GTEx Cortex, PsychENCODE) 1.33e-12 1.07e-10 -2250%
5 139030460-141219083 PCDHA5 (GTEx Thyroid) PCDHA8 (GTEx Cerebellar Hemisphere), NDUFA2 (CMC DLPFC) 6.55e-08 1.37e-06 87.7%
6 25193720-29216321 ZNF184 (GTEx Caudate), PRSS16 (GTEx Cerebellar Hemisphere, GTEx Pituitary), ZSCAN9 (GTEx Cerebellum), ZSCAN23 (GTEx Hypothalamus), BTN3A2 (NTR Blood), ZSCAN12P1 (PsychENCODE) RP5-874C20.3 (GTEx Adrenal Gland, GTEx Cerebellum, GTEx Hippocampus, GTEx Putamen, GTEx Thyroid, GTEx Whole Blood), ZNF204P (GTEx Adrenal Gland), ZKSCAN3 (GTEx Amygdala, GTEx Hippocampus, GTEx Thyroid), ZSCAN31 (GTEx Amygdala), PGBD1 (GTEx Cerebellar Hemisphere), BTN3A2 (GTEx Cerebellar Hemisphere, GTEx Hippocampus, GTEx Pituitary, GTEx Thyroid, GTEx Whole Blood), PRSS16 (GTEx Cerebellum, GTEx Frontal Cortex, GTEx Whole Blood), HMGN4 (GTEx Cerebellum), ZSCAN9 (GTEx Hippocampus, GTEx Pituitary), RP1-265C24.5 (GTEx Hippocampus), RP1-153G14.4 (GTEx Hippocampus), ZNF184 (GTEx Hypothalamus), ZSCAN23 (GTEx Putamen, GTEx Pituitary), HIST1H4D (NTR Blood), HIST1H4L (NTR Blood), NKAPL (PsychENCODE), ZSCAN12P1 (GTEx Whole Blood), ZSCAN16 (YFS Blood) 2.53e-10 1.34e-10 100%
6 30577966-32580366 BAG6 (CMC DLPFC Splicing), MICB (GTEx Thyroid), HCP5 (GTEx Thyroid) PPP1R18 (GTEx Adrenal Gland), FLOT1 (GTEx Cerebellum, CMC DLPFC Splicing, GTEx Pituitary, GTEx Thyroid), VARS2 (GTEx Cortex, GTEx Whole Blood), DDAH2 (GTEx Frontal Cortex, CMC DLPFC), WASF5P (GTEx Pituitary) 1.55e-10 3.95e-08 86%
6 98832858-100829135 COQ3 (CMC DLPFC Splicing) - 2.65e-07 9.09e-08 35.1%
6 104405706-106583999 BVES-AS1 (GTEx Amygdala) LIN28B (CMC DLPFC, PsychENCODE) 2.43e-08 9.50e-08 92.9%
7 11252396-13282905 TMEM106B (PsychENCODE) TMEM106B (GTEx Adrenal Gland, GTEx Whole Blood, YFS Blood) 7.01e-09 2.55e-08 100%
7 24021857-26019767 OSBPL3 (GTEx Pituitary) - 1.88e-08 6.49e-07 77.7%
8 51238261-53720740 PXDNL (CMC DLPFC) - 3.92e-09 1.34e-07 83.8%
8 60435234-62428932 RP11-163N6.2 (GTEx Thyroid) - 9.47e-08 5.25e-07 89.8%
9 125606617-127604411 PIGFP2 (PsychENCODE) - 1.12e-07 2.73e-08 63.8%
11 56092913-58422547 TNKS1BP1 (GTEx Adrenal Gland), CLP1 (GTEx Whole Blood) AP000662.4 (GTEx Thyroid) 2.04e-07 1.47e-07 95.2%
11 60540194-62557903 TMEM258 (PsychENCODE) - 5.12e-07 4.26e-07 83.9%
11 112346414-114345882 DRD2 (GTEx Frontal Cortex) - 3.90e-07 4.90e-07 0.414%
13 52652520-54625616 OLFM4 (CMC DLPFC) - 3.56e-07 6.06e-19 29.9%
14 41077086-43073683 CTD-2298J14.2 (GTEx Thyroid) LRFN5 (GTEx Cerebellar Hemisphere, GTEx Cerebellum) 1.36e-08 2.57e-09 88.1%
14 58952573-61334943 CCDC175 (GTEx Thyroid) RTN1 (CMC DLPFC Splicing, GTEx Thyroid), JKAMP (GTEx Thyroid) 4.28e-08 2.18e-07 82.3%
14 63322572-65770213 ESR2 (GTEx Pituitary) SYNE2 (NTR Blood), ESR2 (GTEx Whole Blood) 2.20e-09 7.60e-10 80%
14 74120633-76388050 PROX2 (GTEx Thyroid) RPS6KL1 (CMC DLPFC Splicing, PsychENCODE), DLST (CMC DLPFC, PsychENCODE), AREL1 (PsychENCODE) 8.51e-09 6.71e-09 93.5%
14 102878783-105180229 RP11-894P9.2 (GTEx Thyroid) RP11-73M18.8 (GTEx Amygdala), AL049840.1 (GTEx Cerebellum, GTEx Cortex), RP11-73M18.9 (GTEx Cortex, PsychENCODE), TRMT61A (CMC DLPFC, GTEx Whole Blood), RP11-600F24.2 (PsychENCODE), RP11-73M18.7 (PsychENCODE), RP11-73M18.6 (PsychENCODE), CKB (YFS Blood) 4.69e-08 3.05e-09 84.6%
16 71147494-73210261 PMFBP1 (PsychENCODE) RP11-73M18.8 (GTEx Amygdala), AL049840.1 (GTEx Cerebellum, GTEx Cortex), RP11-73M18.9 (GTEx Cortex, PsychENCODE), TRMT61A (CMC DLPFC, GTEx Whole Blood), RP11-600F24.2 (PsychENCODE), RP11-73M18.7 (PsychENCODE), RP11-73M18.6 (PsychENCODE), CKB (YFS Blood) 2.46e-07 3.35e-08 76.3%
17 26406423-28478661 TIAF1 (GTEx Adrenal Gland) MYO18A (GTEx Adrenal Gland) 8.27e-08 8.51e-09 58.5%
17 64524284-66521332 CTD-2653B5.1 (PsychENCODE) - 3.30e-07 5.39e-06 25.8%
18 51385406-53561919 RAB27B (PsychENCODE) RAB27B (CMC DLPFC Splicing) 5.36e-07 3.62e-11 14.6%
20 46838019-48853908 DDX27 (CMC DLPFC) SF3B1 (GTEx Hypothalamus) 1.32e-06 3.54e-06 91%
22 40218102-42697216 ZC3H7B (GTEx Cerebellum) EP300 (GTEx Cerebellum, YFS Blood), XPNPEP3 (GTEx Frontal Cortex, CMC DLPFC), SLC25A17 (GTEx Nucleus accumbens, GTEx Thyroid), RANGAP1 (CMC DLPFC Splicing, PsychENCODE) 1.01e-08 7.56e-09 95.5%

Show brief conditional analysis table

MDD TWAS Brief Conditional Results
CHR BP Jointly sign. Features (SNP-weight set) N Marginal Top TWAS p-value Top GWAS p-value Variance Explained
1 7413452-9875347 RERE (YFS Blood) 3 1.10e-07 3.18e-08 100%
1 35885799-37876701 SNORA63 (GTEx Nucleus accumbens) 0 1.24e-06 6.27e-08 69.3%
1 71753372-73766162 NEGR1 (GTEx Whole Blood) 5 1.94e-18 4.54e-15 97.8%
1 174891875-177103690 RFWD2 (CMC DLPFC Splicing) 3 4.66e-07 2.30e-07 90%
1 180725304-182724241 CACNA1E (CMC DLPFC Splicing) 0 6.06e-07 1.08e-07 64%
1 196478918-198741422 DENND1B (CMC DLPFC Splicing) 2 5.90e-08 3.11e-08 92.6%
2 57388379-59467945 FANCL (CMC DLPFC Splicing, CMC DLPFC) 0 2.18e-07 4.68e-09 85%
2 196832647-199295649 ANKRD44 (YFS Blood) 1 1.27e-08 3.52e-07 82.8%
3 43487406-45561063 ZNF445 (CMC DLPFC) 8 3.34e-07 6.34e-08 74.7%
4 40937584-43090938 SLC30A9 (GTEx Cortex), TMEM33 (PsychENCODE) 8 7.72e-09 3.59e-09 92.3%
5 86565927-88989352 TMEM161B-AS1 (GTEx Caudate, PsychENCODE), CTC-467M3.3 (GTEx Frontal Cortex), CTC-498M16.4 (GTEx Substantia nigra) 18 1.33e-12 1.07e-10 -2250%
5 139030460-141219083 PCDHA5 (GTEx Thyroid) 2 6.55e-08 1.37e-06 87.7%
6 25193720-29216321 ZNF184 (GTEx Caudate), PRSS16 (GTEx Cerebellar Hemisphere, GTEx Pituitary), ZSCAN9 (GTEx Cerebellum), ZSCAN23 (GTEx Hypothalamus), BTN3A2 (NTR Blood), ZSCAN12P1 (PsychENCODE) 33 2.53e-10 1.34e-10 100%
6 30577966-32580366 BAG6 (CMC DLPFC Splicing), MICB (GTEx Thyroid), HCP5 (GTEx Thyroid) 12 1.55e-10 3.95e-08 86%
6 98832858-100829135 COQ3 (CMC DLPFC Splicing) 0 2.65e-07 9.09e-08 35.1%
6 104405706-106583999 BVES-AS1 (GTEx Amygdala) 2 2.43e-08 9.50e-08 92.9%
7 11252396-13282905 TMEM106B (PsychENCODE) 3 7.01e-09 2.55e-08 100%
7 24021857-26019767 OSBPL3 (GTEx Pituitary) 0 1.88e-08 6.49e-07 77.7%
8 51238261-53720740 PXDNL (CMC DLPFC) 0 3.92e-09 1.34e-07 83.8%
8 60435234-62428932 RP11-163N6.2 (GTEx Thyroid) 0 9.47e-08 5.25e-07 89.8%
9 125606617-127604411 PIGFP2 (PsychENCODE) 0 1.12e-07 2.73e-08 63.8%
11 56092913-58422547 TNKS1BP1 (GTEx Adrenal Gland), CLP1 (GTEx Whole Blood) 1 2.04e-07 1.47e-07 95.2%
11 60540194-62557903 TMEM258 (PsychENCODE) 0 5.12e-07 4.26e-07 83.9%
11 112346414-114345882 DRD2 (GTEx Frontal Cortex) 0 3.90e-07 4.90e-07 0.414%
13 52652520-54625616 OLFM4 (CMC DLPFC) 0 3.56e-07 6.06e-19 29.9%
14 41077086-43073683 CTD-2298J14.2 (GTEx Thyroid) 2 1.36e-08 2.57e-09 88.1%
14 58952573-61334943 CCDC175 (GTEx Thyroid) 3 4.28e-08 2.18e-07 82.3%
14 63322572-65770213 ESR2 (GTEx Pituitary) 2 2.20e-09 7.60e-10 80%
14 74120633-76388050 PROX2 (GTEx Thyroid) 5 8.51e-09 6.71e-09 93.5%
14 102878783-105180229 RP11-894P9.2 (GTEx Thyroid) 11 4.69e-08 3.05e-09 84.6%
16 71147494-73210261 PMFBP1 (PsychENCODE) 11 2.46e-07 3.35e-08 76.3%
17 26406423-28478661 TIAF1 (GTEx Adrenal Gland) 1 8.27e-08 8.51e-09 58.5%
17 64524284-66521332 CTD-2653B5.1 (PsychENCODE) 0 3.30e-07 5.39e-06 25.8%
18 51385406-53561919 RAB27B (PsychENCODE) 1 5.36e-07 3.62e-11 14.6%
20 46838019-48853908 DDX27 (CMC DLPFC) 1 1.32e-06 3.54e-06 91%
22 40218102-42697216 ZC3H7B (GTEx Cerebellum) 8 1.01e-08 7.56e-09 95.5%

Show novelty table

MDD TWAS Results Novelty
CHR BP ID PANEL WGT TWAS.P BEST.GWAS.P TOP.SNP.COR TYPE NOVEL NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA NA
YFS.BLOOD.RNAARR/YFS.RERE.wgt.RDat YFS.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.RERE.wgt.RDat RERE 1 0.08894 rs301819 5.510 rs301806 0.153527 -13.99 5.18200 521 4 lasso 0.157000 0.00e+00 -5.310078 1.10e-07 0.000 0.001 0.000 0.006 0.993 TRUE -0.97 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR 8412457 8877702 YFS Blood Joint No 8412457-8877702 3.588337e-08 TRUE
Whole_Blood/Whole_Blood.ENSG00000142599.13.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000142599.13.wgt.RDat RERE 1 0.24580 rs301819 5.510 rs301806 0.175000 -8.47 5.18200 376 4 lasso 0.173923 3.89e-17 -5.095707 3.47e-07 0.000 0.001 0.000 0.006 0.993 FALSE -0.98 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 8412457 8877702 GTEx Whole Blood Marginal No 8412457-8877702 3.588337e-08 TRUE
Thyroid/Thyroid.ENSG00000232912.1.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000232912.1.wgt.RDat RP5-1115A15.1 1 0.11740 rs301819 5.510 rs301805 0.087900 -6.47 5.36500 306 4 lasso 0.100301 6.06e-11 -5.175240 2.28e-07 0.000 0.001 0.000 0.004 0.995 FALSE -0.94 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 8484705 8494898 GTEx Thyroid Marginal No 8484705-8494898 3.588337e-08 TRUE
Whole_Blood/Whole_Blood.ENSG00000232912.1.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000232912.1.wgt.RDat RP5-1115A15.1 1 0.05580 rs301819 5.510 rs301805 -0.002120 -3.68 5.36500 306 306 blup 0.006669 6.35e-02 -4.866386 1.14e-06 0.012 0.001 0.088 0.008 0.891 FALSE -0.75 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 8484705 8494898 GTEx Whole Blood Marginal No 8484705-8494898 3.588337e-08 TRUE
Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000201448.1.wgt.RDat Brain_Nucleus_accumbens_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000201448.1.wgt.RDat SNORA63 1 0.19900 rs1002656 5.380 rs7544015 -0.007660 3.62 3.19800 371 371 blup 0.006930 1.71e-01 4.848870 1.24e-06 0.067 0.006 0.257 0.023 0.647 TRUE 0.64 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia 36884051 36884179 GTEx Nucleus accumbens Joint Yes 36884051-36884179 7.448584e-08 FALSE
Whole_Blood/Whole_Blood.ENSG00000172260.9.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000172260.9.wgt.RDat NEGR1 1 0.13050 rs7531118 -7.810 rs11209948 0.069200 -5.78 -7.60100 319 15 enet 0.112708 2.29e-11 8.760622 1.94e-18 0.000 0.000 0.000 0.007 0.993 TRUE -0.82 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 71861623 72748417 GTEx Whole Blood Joint No 71861623-72748417 5.718799e-15 TRUE
Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000172260.9.wgt.RDat Brain_Caudate_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000172260.9.wgt.RDat NEGR1 1 0.16780 rs7531118 -7.810 rs12759396 0.001590 -3.72 -6.11000 319 319 blup 0.055669 2.64e-03 5.780100 7.47e-09 0.000 0.000 0.284 0.036 0.681 FALSE -0.69 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia 71861623 72748417 GTEx Caudate Marginal No 71861623-72748417 5.718799e-15 FALSE
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000172260.9.wgt.RDat Brain_Putamen_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000172260.9.wgt.RDat NEGR1 1 0.30300 rs7531118 -7.810 rs2012697 0.194919 -4.83 -7.51000 318 20 enet 0.251490 1.44e-08 5.548510 2.88e-08 0.000 0.000 0.018 0.014 0.968 FALSE -0.82 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia 71861623 72748417 GTEx Putamen Marginal No 71861623-72748417 5.718799e-15 TRUE
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000227207.2.wgt.RDat Brain_Cerebellar_Hemisphere /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000227207.2.wgt.RDat RPL31P12 1 0.46500 rs7531118 -7.810 rs11209948 0.524000 8.05 -7.60100 321 6 lasso 0.522525 1.56e-21 -7.785520 6.94e-15 0.000 0.000 0.000 0.010 0.990 FALSE 0.99 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere 72767155 72767512 GTEx Cerebellar Hemisphere Marginal No 72767155-72767512 5.718799e-15 TRUE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000227207.2.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000227207.2.wgt.RDat RPL31P12 1 0.63030 rs7531118 -7.810 rs2568958 0.567000 9.31 -7.74100 321 3 lasso 0.544830 8.24e-28 -7.708820 1.27e-14 0.000 0.000 0.000 0.006 0.994 FALSE 1.00 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 72767155 72767512 GTEx Cerebellum Marginal No 72767155-72767512 5.718799e-15 TRUE
PEC_TWAS_weights/ENSG00000227207.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000227207.wgt.RDat RPL31P12 1 0.17714 rs1432639 -7.839 rs2568960 0.225000 17.46 -7.76900 1184 10 enet 0.227000 0.00e+00 -7.742756 9.73e-15 0.000 0.000 0.000 0.007 0.993 FALSE 0.99 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 72767155 72767512 PsychENCODE Marginal No 72767155-72767512 4.541485e-15 TRUE
Thyroid/Thyroid.ENSG00000227740.1.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000227740.1.wgt.RDat RP11-318C24.2 1 0.04890 rs10913112 -5.170 rs6680839 0.037700 -4.51 4.60700 394 3 lasso 0.041100 2.73e-05 -5.027510 4.97e-07 0.004 0.007 0.012 0.018 0.959 FALSE 0.88 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 175873898 175889649 GTEx Thyroid Marginal Yes 175873898-175889649 2.340940e-07 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176104146:clu_42334.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176104146:clu_42334.wgt.RDat RFWD2 1 0.13900 rs10913112 -5.170 rs10436856 0.231000 -10.23 -5.01400 366 3 lasso 0.253891 4.06e-30 5.039850 4.66e-07 0.000 0.007 0.000 0.020 0.973 TRUE -0.86 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 175913966 176176370 CMC DLPFC Splicing Joint Yes 175913966-176176370 2.340940e-07 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176102983:clu_42334.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176085817:176102983:clu_42334.wgt.RDat RFWD2 1 0.13430 rs10913112 -5.170 rs10753117 0.209000 9.69 -4.72600 365 4 lasso 0.210674 1.10e-24 -4.958690 7.10e-07 0.000 0.008 0.000 0.021 0.971 FALSE 0.93 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 175913966 176176370 CMC DLPFC Splicing Marginal Yes 175913966-176176370 2.340940e-07 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176103036:176104146:clu_42334.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:176103036:176104146:clu_42334.wgt.RDat RFWD2 1 0.12220 rs10913112 -5.170 rs10436856 0.168000 9.29 -5.01400 356 4 lasso 0.214117 4.15e-25 -5.005960 5.56e-07 0.000 0.007 0.000 0.019 0.973 FALSE 0.84 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 175913966 176176370 CMC DLPFC Splicing Marginal Yes 175913966-176176370 2.340940e-07 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:181724533:181725092:clu_42439.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:181724533:181725092:clu_42439.wgt.RDat CACNA1E 1 0.11150 rs2332571 5.310 rs4652678 0.083800 -6.59 3.70200 490 4 lasso 0.086921 1.48e-10 -4.989390 6.06e-07 0.000 0.151 0.000 0.420 0.429 TRUE -0.52 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 181452685 181775921 CMC DLPFC Splicing Joint Yes 181452685-181775921 1.096252e-07 FALSE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197704716:clu_42580.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197704716:clu_42580.wgt.RDat DENND1B 1 0.15900 rs16841842 -5.350 rs17641524 0.180000 9.05 -5.29200 338 6 lasso 0.172047 4.55e-20 -5.421950 5.90e-08 0.000 0.001 0.000 0.010 0.989 TRUE 0.90 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 197473878 197744623 CMC DLPFC Splicing Joint Yes 197473878-197744623 8.795423e-08 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197741998:clu_42580.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr1:197684204:197741998:clu_42580.wgt.RDat DENND1B 1 0.10260 rs16841842 -5.350 rs16841904 0.153000 -8.49 -5.21100 354 8 lasso 0.144111 7.46e-17 5.018050 5.22e-07 0.000 0.001 0.000 0.010 0.989 FALSE -0.89 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 197473878 197744623 CMC DLPFC Splicing Marginal Yes 197473878-197744623 8.795423e-08 TRUE
CMC.BRAIN.RNASEQ/CMC.DENND1B.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.DENND1B.wgt.RDat DENND1B 1 0.07270 rs16841842 -5.350 rs16841842 0.050100 -5.76 -5.34600 417 417 blup 0.055662 2.33e-07 4.848374 1.24e-06 0.000 0.001 0.000 0.011 0.988 FALSE -0.78 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 197473878 197744623 CMC DLPFC Marginal Yes 197473878-197744623 8.795423e-08 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr2:58388773:58390001:clu_36265.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr2:58388773:58390001:clu_36265.wgt.RDat FANCL 2 0.06710 rs11682175 5.860 rs10172295 0.033604 4.78 4.38500 347 347 blup 0.040895 1.07e-05 4.897476 9.71e-07 0.000 0.001 0.007 0.104 0.888 TRUE 0.52 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 58386377 58468515 CMC DLPFC Splicing Joint No 58386377-58468515 4.628672e-09 TRUE
CMC.BRAIN.RNASEQ/CMC.FANCL.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.FANCL.wgt.RDat FANCL 2 0.06110 rs11682175 5.860 rs11682175 0.008634 -3.97 5.85800 387 387 blup 0.029378 1.48e-04 -5.183180 2.18e-07 0.001 0.000 0.056 0.027 0.916 TRUE -0.64 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 58386377 58468515 CMC DLPFC Joint No 58386377-58468515 4.628672e-09 TRUE
YFS.BLOOD.RNAARR/YFS.ANKRD44.wgt.RDat YFS.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.ANKRD44.wgt.RDat ANKRD44 2 0.02219 rs10931791 4.940 rs2256931 -0.000728 -3.70 1.89600 416 416 blup 0.008445 6.29e-04 -5.690140 1.27e-08 0.062 0.023 0.168 0.061 0.686 TRUE -0.60 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR 197831741 198175897 YFS Blood Joint Yes 197831741-198175897 7.812257e-07 FALSE
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000115524.11.wgt.RDat Brain_Hypothalamus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000115524.11.wgt.RDat SF3B1 2 0.18700 rs7557203 5.030 rs700655 0.021790 3.90 4.26100 328 328 blup 0.064130 4.90e-03 5.214900 1.84e-07 0.071 0.015 0.319 0.068 0.526 FALSE 0.81 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus 198254508 198299815 GTEx Hypothalamus Marginal Yes 198254508-198299815 4.904798e-07 FALSE
CMC.BRAIN.RNASEQ/CMC.ZNF445.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.ZNF445.wgt.RDat ZNF445 3 0.06220 rs11707582 5.330 rs7616113 0.035714 -5.55 3.61700 353 353 blup 0.060170 7.65e-08 -5.103280 3.34e-07 0.000 0.087 0.002 0.601 0.310 TRUE -0.66 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 44481261 44561226 CMC DLPFC Joint Yes 44481261-44561226 9.821277e-08 FALSE
PEC_TWAS_weights/ENSG00000109133.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000109133.wgt.RDat TMEM33 4 0.09793 rs13146152 -5.590 rs9990708 0.031300 -7.50 -5.28500 1527 14 enet 0.054006 7.05e-18 4.837418 1.32e-06 0.000 0.001 0.000 0.074 0.925 TRUE -0.42 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 41937137 41962589 PsychENCODE Joint No 41937137-41962589 2.270696e-08 TRUE
Thyroid/Thyroid.ENSG00000182308.5.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000182308.5.wgt.RDat DCAF4L1 4 0.06810 rs16854051 -5.900 rs4861156 0.044500 4.39 -5.12800 423 1 lasso 0.032600 1.73e-04 -5.128000 2.93e-07 0.003 0.001 0.291 0.091 0.615 FALSE 0.64 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 41983713 41988476 GTEx Thyroid Marginal No 41983713-41988476 3.635016e-09 FALSE
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000272862.1.wgt.RDat Brain_Cerebellar_Hemisphere /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000272862.1.wgt.RDat RP11-814H16.2 4 0.16500 rs16854051 -5.900 rs6848386 0.035484 -3.58 -4.79600 424 5 lasso 0.044330 1.08e-02 5.009600 5.45e-07 0.005 0.000 0.561 0.055 0.378 FALSE -0.74 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere 41990758 41991254 GTEx Cerebellar Hemisphere Marginal No 41990758-41991254 3.635016e-09 FALSE
Brain_Cortex/Brain_Cortex.ENSG00000014824.9.wgt.RDat Brain_Cortex /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000014824.9.wgt.RDat SLC30A9 4 0.27030 rs16854051 -5.900 rs4377621 0.016520 3.94 -4.91100 424 424 blup 0.063090 1.92e-03 -5.774530 7.72e-09 0.003 0.001 0.340 0.113 0.543 TRUE 0.73 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex 41992489 42092474 GTEx Cortex Joint No 41992489-42092474 3.635016e-09 FALSE
Brain_Amygdala/Brain_Amygdala.ENSG00000014824.9.wgt.RDat Brain_Amygdala /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000014824.9.wgt.RDat SLC30A9 4 0.28400 rs16854051 -5.900 rs1507086 -0.011800 3.52 -4.45600 424 424 blup 0.064540 1.00e-02 -5.253400 1.49e-07 0.004 0.001 0.476 0.070 0.450 FALSE 0.79 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala 41992489 42092474 GTEx Amygdala Marginal No 41992489-42092474 3.635016e-09 FALSE
Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000014824.9.wgt.RDat Brain_Anterior_cingulate_cortex_BA24 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000014824.9.wgt.RDat SLC30A9 4 0.15160 rs16854051 -5.900 rs4377621 0.074069 3.86 -4.91100 424 424 blup 0.053870 8.95e-03 -5.001690 5.68e-07 0.003 0.001 0.399 0.101 0.496 FALSE 0.81 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24 41992489 42092474 GTEx ACC Marginal No 41992489-42092474 3.635016e-09 FALSE
Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000014824.9.wgt.RDat Brain_Caudate_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000014824.9.wgt.RDat SLC30A9 4 0.14720 rs16854051 -5.900 rs1507086 0.018708 3.85 -4.45600 424 3 enet 0.013800 8.61e-02 -4.854800 1.21e-06 0.003 0.001 0.388 0.086 0.521 FALSE 0.78 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia 41992489 42092474 GTEx Caudate Marginal No 41992489-42092474 3.635016e-09 FALSE
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000014824.9.wgt.RDat Brain_Hypothalamus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000014824.9.wgt.RDat SLC30A9 4 0.27600 rs16854051 -5.900 rs4861163 0.113100 4.68 -5.13000 424 424 blup 0.146040 2.89e-05 -5.085140 3.67e-07 0.000 0.000 0.016 0.039 0.944 FALSE 0.83 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus 41992489 42092474 GTEx Hypothalamus Marginal No 41992489-42092474 3.635016e-09 TRUE
Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000014824.9.wgt.RDat Brain_Nucleus_accumbens_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000014824.9.wgt.RDat SLC30A9 4 0.24800 rs16854051 -5.900 rs1983138 0.071800 4.58 -4.88700 424 424 blup 0.080020 6.80e-04 -5.602700 2.11e-08 0.001 0.001 0.072 0.119 0.808 FALSE 0.82 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia 41992489 42092474 GTEx Nucleus accumbens Marginal No 41992489-42092474 3.635016e-09 TRUE
PEC_TWAS_weights/ENSG00000014824.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000014824.wgt.RDat SLC30A9 4 0.28664 rs13146152 -5.590 rs4861157 0.122000 12.78 -5.55500 1626 43 enet 0.233893 0.00e+00 -5.259200 1.45e-07 0.000 0.000 0.000 0.026 0.974 FALSE 0.76 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 41992489 42092474 PsychENCODE Marginal No 41992489-42092474 2.270696e-08 TRUE
PEC_TWAS_weights/ENSG00000247828.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000247828.wgt.RDat TMEM161B-AS1 5 0.30403 rs27732 6.371 rs112055376 0.293000 19.82 6.03823 1178 1178 bslmm 0.303307 0.00e+00 6.091010 1.12e-09 0.000 0.000 0.000 0.117 0.883 TRUE 0.57 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 87564712 87732502 PsychENCODE Joint No 87564712-87732502 1.877996e-10 TRUE
Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000247828.3.wgt.RDat Brain_Caudate_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.28690 rs10044618 6.390 rs780404 0.302955 6.60 6.02700 259 11 lasso 0.269805 1.80e-11 6.282167 3.34e-10 0.000 0.000 0.000 0.062 0.938 FALSE 0.67 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia 87564888 87732502 GTEx Caudate Joint No 87564888-87732502 1.658858e-10 TRUE
Adrenal_Gland/Adrenal_Gland.ENSG00000247828.3.wgt.RDat Adrenal_Gland /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.31160 rs10044618 6.390 rs415302 0.269660 7.18 5.49800 274 11 lasso 0.286037 1.76e-14 5.360090 8.32e-08 0.000 0.000 0.000 0.086 0.914 FALSE 0.52 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland 87564888 87732502 GTEx Adrenal Gland Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Amygdala/Brain_Amygdala.ENSG00000247828.3.wgt.RDat Brain_Amygdala /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.38600 rs10044618 6.390 rs780404 0.287340 5.49 6.02700 256 10 lasso 0.278270 9.13e-08 6.118500 9.45e-10 0.000 0.000 0.004 0.058 0.938 FALSE 0.58 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala 87564888 87732502 GTEx Amygdala Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000247828.3.wgt.RDat Brain_Anterior_cingulate_cortex_BA24 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.37620 rs10044618 6.390 rs390856 0.306000 6.21 6.00300 256 9 lasso 0.317250 1.33e-10 6.445500 1.15e-10 0.000 0.000 0.000 0.056 0.944 FALSE 0.69 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24 87564888 87732502 GTEx ACC Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000247828.3.wgt.RDat Brain_Cerebellar_Hemisphere /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.33450 rs10044618 6.390 rs780404 0.233104 6.00 6.02700 257 10 lasso 0.233430 7.93e-09 6.011700 1.84e-09 0.000 0.000 0.000 0.062 0.938 TRUE 0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere 87564888 87732502 GTEx Cerebellar Hemisphere Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000247828.3.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.36980 rs10044618 6.390 rs780404 0.320000 7.24 6.02700 263 4 lasso 0.323363 1.06e-14 6.053050 1.42e-09 0.000 0.000 0.000 0.052 0.948 FALSE 0.58 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 87564888 87732502 GTEx Cerebellum Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Cortex/Brain_Cortex.ENSG00000247828.3.wgt.RDat Brain_Cortex /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.38200 rs10044618 6.390 rs11743103 0.319615 6.92 5.98300 258 11 lasso 0.337950 8.68e-14 6.021420 1.73e-09 0.000 0.000 0.000 0.070 0.930 FALSE 0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex 87564888 87732502 GTEx Cortex Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000247828.3.wgt.RDat Brain_Frontal_Cortex_BA9 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.30390 rs10044618 6.390 rs780404 0.287968 6.15 6.02700 259 24 enet 0.295280 1.47e-10 6.720000 1.82e-11 0.000 0.000 0.000 0.086 0.914 FALSE 0.70 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 87564888 87732502 GTEx Frontal Cortex Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000247828.3.wgt.RDat Brain_Hypothalamus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.39800 rs10044618 6.390 rs780404 0.336574 6.07 6.02700 255 5 lasso 0.313540 2.17e-10 5.875800 4.21e-09 0.000 0.000 0.000 0.060 0.940 FALSE 0.57 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus 87564888 87732502 GTEx Hypothalamus Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000247828.3.wgt.RDat Brain_Nucleus_accumbens_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.33100 rs10044618 6.390 rs780404 0.266183 6.16 6.02700 257 8 lasso 0.266900 2.21e-10 6.010490 1.85e-09 0.000 0.000 0.000 0.059 0.941 FALSE 0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia 87564888 87732502 GTEx Nucleus accumbens Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000247828.3.wgt.RDat Brain_Putamen_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.45800 rs10044618 6.390 rs780404 0.332685 6.08 6.02700 257 8 lasso 0.285111 1.14e-09 6.372050 1.87e-10 0.000 0.000 0.000 0.054 0.946 FALSE 0.65 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia 87564888 87732502 GTEx Putamen Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000247828.3.wgt.RDat Brain_Substantia_nigra /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Substantia_nigra/Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.46000 rs10044618 6.390 rs780404 0.333000 5.41 6.02700 256 6 lasso 0.323370 2.75e-08 6.057270 1.38e-09 0.000 0.000 0.009 0.054 0.937 FALSE 0.60 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Substantia_nigra 87564888 87732502 GTEx Substantia nigra Marginal No 87564888-87732502 1.658858e-10 TRUE
Pituitary/Pituitary.ENSG00000247828.3.wgt.RDat Pituitary /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.42890 rs10044618 6.390 rs13172095 0.434966 8.35 6.05800 260 2 lasso 0.427610 1.30e-20 6.048500 1.46e-09 0.000 0.000 0.000 0.050 0.950 FALSE 0.58 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary 87564888 87732502 GTEx Pituitary Marginal No 87564888-87732502 1.658858e-10 TRUE
Thyroid/Thyroid.ENSG00000247828.3.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.20080 rs10044618 6.390 rs13172095 0.223000 9.66 6.05800 274 7 lasso 0.229150 2.09e-24 5.889760 3.87e-09 0.000 0.000 0.000 0.079 0.920 FALSE 0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 87564888 87732502 GTEx Thyroid Marginal No 87564888-87732502 1.658858e-10 TRUE
Whole_Blood/Whole_Blood.ENSG00000247828.3.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000247828.3.wgt.RDat TMEM161B-AS1 5 0.12240 rs10044618 6.390 rs4916899 0.042902 5.54 6.01100 274 6 lasso 0.063570 5.72e-07 5.526440 3.27e-08 0.000 0.000 0.000 0.048 0.952 TRUE 0.53 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 87564888 87732502 GTEx Whole Blood Marginal No 87564888-87732502 1.658858e-10 TRUE
Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000271904.1.wgt.RDat Brain_Substantia_nigra /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Substantia_nigra/Brain_Substantia_nigra/Brain_Substantia_nigra.ENSG00000271904.1.wgt.RDat CTC-498M16.4 5 0.20700 rs10044618 6.390 rs10044618 0.005520 3.82 6.38700 255 255 blup 0.055980 2.02e-02 5.403610 6.53e-08 0.000 0.000 0.391 0.037 0.572 TRUE 0.82 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Substantia_nigra 87729709 87794514 GTEx Substantia nigra Joint No 87729709-87794514 1.658858e-10 FALSE
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000250377.1.wgt.RDat Brain_Frontal_Cortex_BA9 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000250377.1.wgt.RDat CTC-467M3.3 5 0.18240 rs10044618 6.390 rs1081158 0.013998 -4.20 6.19200 267 267 blup 0.052300 7.53e-03 -7.091600 1.33e-12 0.000 0.000 0.114 0.035 0.850 TRUE -0.45 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 87988462 87989789 GTEx Frontal Cortex Joint No 87988462-87989789 1.658858e-10 TRUE
Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000250377.1.wgt.RDat Brain_Anterior_cingulate_cortex_BA24 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24/Brain_Anterior_cingulate_cortex_BA24.ENSG00000250377.1.wgt.RDat CTC-467M3.3 5 0.21470 rs10044618 6.390 rs2304607 0.019300 -3.67 5.78200 266 266 blup 0.113220 2.18e-04 -5.813700 6.11e-09 0.000 0.000 0.631 0.053 0.317 FALSE -0.67 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Anterior_cingulate_cortex_BA24 87988462 87989789 GTEx ACC Marginal No 87988462-87989789 1.658858e-10 FALSE
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000250377.1.wgt.RDat Brain_Cerebellar_Hemisphere /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000250377.1.wgt.RDat CTC-467M3.3 5 0.12920 rs10044618 6.390 rs454214 0.019687 -4.57 6.22200 266 5 lasso 0.021460 5.68e-02 -5.861000 4.60e-09 0.000 0.000 0.056 0.051 0.893 FALSE -0.30 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere 87988462 87989789 GTEx Cerebellar Hemisphere Marginal Yes 87988462-87989789 1.658858e-10 TRUE
Brain_Cortex/Brain_Cortex.ENSG00000250377.1.wgt.RDat Brain_Cortex /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000250377.1.wgt.RDat CTC-467M3.3 5 0.22120 rs10044618 6.390 rs34338 0.068029 -4.29 6.30000 267 21 enet 0.068860 1.23e-03 -6.510990 7.47e-11 0.000 0.000 0.091 0.040 0.870 FALSE -0.41 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex 87988462 87989789 GTEx Cortex Marginal No 87988462-87989789 1.658858e-10 TRUE
PEC_TWAS_weights/ENSG00000250377.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000250377.wgt.RDat CTC-467M3.3 5 0.02825 rs27732 6.371 rs7733438 0.006110 -5.04 5.77100 841 3 lasso 0.012112 3.61e-05 -6.097890 1.07e-09 0.000 0.000 0.035 0.251 0.715 FALSE -0.12 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 87988462 87989789 PsychENCODE Marginal Yes 87988462-87989789 1.877996e-10 FALSE
CMC.BRAIN.RNASEQ/CMC.NDUFA2.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.NDUFA2.wgt.RDat NDUFA2 5 0.01750 rs3806843 -4.830 rs12659980 0.007610 -3.56 -4.53300 343 343 bslmm 0.013400 7.81e-03 5.190020 2.10e-07 0.104 0.037 0.170 0.060 0.629 FALSE -0.86 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 140024947 140027370 CMC DLPFC Marginal Yes 140024947-140027370 1.365331e-06 FALSE
Thyroid/Thyroid.ENSG00000204965.4.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000204965.4.wgt.RDat PCDHA5 5 0.06360 rs3806843 -4.830 rs2098058 0.004860 3.84 -4.61500 376 14 enet 0.022560 1.55e-03 -5.402970 6.55e-08 0.055 0.028 0.090 0.046 0.781 TRUE 0.66 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 140201222 140203811 GTEx Thyroid Joint Yes 140201222-140203811 1.365331e-06 FALSE
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000204962.4.wgt.RDat Brain_Cerebellar_Hemisphere /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000204962.4.wgt.RDat PCDHA8 5 0.22650 rs3806843 -4.830 rs2563265 0.121994 5.37 -4.50500 372 372 blup 0.180230 5.33e-07 -4.980100 6.36e-07 0.001 0.032 0.001 0.051 0.915 FALSE 0.86 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere 140220907 140223351 GTEx Cerebellar Hemisphere Marginal Yes 140220907-140223351 1.365331e-06 TRUE
NTR.BLOOD.RNAARR/NTR.HIST1H4D.wgt.RDat NTR.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR/NTR.BLOOD.RNAARR/NTR.HIST1H4D.wgt.RDat HIST1H4D 6 0.01730 rs3799380 -6.350 rs16891464 0.000459 -3.71 0.97800 634 634 bslmm 0.005940 3.75e-03 -4.987600 6.11e-07 0.000 0.000 0.309 0.059 0.632 FALSE 0.42 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR 26188921 26189323 NTR Blood Marginal No 26188921-26189323 2.153149e-10 FALSE
NTR.BLOOD.RNAARR/NTR.BTN3A2.wgt.RDat NTR.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR/NTR.BLOOD.RNAARR/NTR.BTN3A2.wgt.RDat BTN3A2 6 0.39320 rs3799380 -6.350 rs9379851 0.511828 -25.26 -5.44400 532 35 enet 0.517690 0.00e+00 5.326600 1.00e-07 0.000 0.000 0.000 0.562 0.438 FALSE -0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR 26365386 26378540 NTR Blood Joint No 26365386-26378540 2.153149e-10 FALSE
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000186470.9.wgt.RDat Brain_Cerebellar_Hemisphere /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000186470.9.wgt.RDat BTN3A2 6 0.52900 rs3799380 -6.350 rs9366653 0.348070 -6.68 -5.40600 557 15 lasso 0.311590 9.83e-12 5.188200 2.12e-07 0.000 0.000 0.000 0.505 0.495 FALSE -0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere 26365387 26378546 GTEx Cerebellar Hemisphere Marginal No 26365387-26378546 2.153149e-10 FALSE
Brain_Hippocampus/Brain_Hippocampus.ENSG00000186470.9.wgt.RDat Brain_Hippocampus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000186470.9.wgt.RDat BTN3A2 6 0.56000 rs3799380 -6.350 rs9366653 0.245340 -6.29 -5.40600 557 24 lasso 0.318160 8.38e-11 4.963000 6.96e-07 0.000 0.000 0.000 0.506 0.494 FALSE -0.62 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus 26365387 26378546 GTEx Hippocampus Marginal No 26365387-26378546 2.153149e-10 FALSE
Pituitary/Pituitary.ENSG00000186470.9.wgt.RDat Pituitary /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000186470.9.wgt.RDat BTN3A2 6 0.56000 rs3799380 -6.350 rs9366653 0.421860 -8.20 -5.40600 555 21 lasso 0.427690 1.28e-20 5.898930 3.66e-09 0.000 0.000 0.000 0.501 0.499 FALSE -0.64 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary 26365387 26378546 GTEx Pituitary Marginal No 26365387-26378546 2.153149e-10 FALSE
Thyroid/Thyroid.ENSG00000186470.9.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000186470.9.wgt.RDat BTN3A2 6 0.73560 rs3799380 -6.350 rs9366653 0.563545 -15.23 -5.40600 557 40 enet 0.595520 0.00e+00 5.481600 4.22e-08 0.000 0.000 0.000 0.507 0.492 FALSE -0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 26365387 26378546 GTEx Thyroid Marginal No 26365387-26378546 2.153149e-10 FALSE
Whole_Blood/Whole_Blood.ENSG00000186470.9.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000186470.9.wgt.RDat BTN3A2 6 0.64660 rs3799380 -6.350 rs9379851 0.500493 -14.04 -5.44400 557 35 enet 0.541880 0.00e+00 5.086960 3.64e-07 0.000 0.000 0.000 0.543 0.457 FALSE -0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 26365387 26378546 GTEx Whole Blood Marginal No 26365387-26378546 2.153149e-10 FALSE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000182952.4.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000182952.4.wgt.RDat HMGN4 6 0.17300 rs3799380 -6.350 rs9393729 0.020800 3.93 4.08200 486 486 blup 0.012600 8.82e-02 5.395400 6.84e-08 0.000 0.000 0.404 0.299 0.297 FALSE -0.32 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 26538633 26546482 GTEx Cerebellum Marginal No 26538633-26546482 2.153149e-10 FALSE
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000112812.11.wgt.RDat Brain_Cerebellar_Hemisphere /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000112812.11.wgt.RDat PRSS16 6 0.40500 rs6938943 -6.310 rs13219354 0.171270 5.12 -4.22300 339 19 enet 0.139750 1.13e-05 -4.891200 1.00e-06 0.000 0.000 0.096 0.322 0.582 FALSE 0.56 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere 27215480 27224250 GTEx Cerebellar Hemisphere Joint No 27215480-27224250 2.790355e-10 FALSE
Pituitary/Pituitary.ENSG00000112812.11.wgt.RDat Pituitary /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000112812.11.wgt.RDat PRSS16 6 0.12900 rs6938943 -6.310 rs4713096 0.089290 4.21 -5.84100 340 6 lasso 0.073530 3.62e-04 -5.916080 3.30e-09 0.000 0.000 0.044 0.035 0.921 FALSE 0.30 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary 27215480 27224250 GTEx Pituitary Joint Yes 27215480-27224250 2.790355e-10 TRUE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000112812.11.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000112812.11.wgt.RDat PRSS16 6 0.34700 rs6938943 -6.310 rs13219354 0.193950 6.56 -4.22300 340 4 lasso 0.234300 1.39e-10 -4.947900 7.50e-07 0.000 0.001 0.000 0.962 0.037 FALSE 0.55 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 27215480 27224250 GTEx Cerebellum Marginal No 27215480-27224250 2.790355e-10 FALSE
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000112812.11.wgt.RDat Brain_Frontal_Cortex_BA9 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000112812.11.wgt.RDat PRSS16 6 0.18900 rs6938943 -6.310 rs9348772 0.046080 3.93 -3.08200 341 18 enet 0.055850 5.93e-03 -5.045000 4.54e-07 0.000 0.000 0.366 0.056 0.577 FALSE 0.48 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 27215480 27224250 GTEx Frontal Cortex Marginal No 27215480-27224250 2.790355e-10 FALSE
Whole_Blood/Whole_Blood.ENSG00000112812.11.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000112812.11.wgt.RDat PRSS16 6 0.08520 rs6938943 -6.310 rs6913660 0.059488 5.11 -5.15500 342 4 lasso 0.043950 2.99e-05 -5.335920 9.51e-08 0.000 0.001 0.047 0.474 0.479 FALSE 0.47 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 27215480 27224250 GTEx Whole Blood Marginal No 27215480-27224250 2.790355e-10 FALSE
Adrenal_Gland/Adrenal_Gland.ENSG00000204789.3.wgt.RDat Adrenal_Gland /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000204789.3.wgt.RDat ZNF204P 6 0.14100 rs6938943 -6.310 rs201004 0.059999 4.34 -4.72200 395 7 lasso 0.050060 1.74e-03 -5.032700 4.84e-07 0.000 0.000 0.547 0.211 0.242 FALSE 0.60 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland 27325604 27339304 GTEx Adrenal Gland Marginal No 27325604-27339304 2.790355e-10 FALSE
Brain_Hippocampus/Brain_Hippocampus.ENSG00000271755.1.wgt.RDat Brain_Hippocampus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000271755.1.wgt.RDat RP1-153G14.4 6 0.15900 rs6938943 -6.310 rs10946940 0.040750 -3.92 -4.17000 410 410 blup 0.051320 9.89e-03 5.354000 8.60e-08 0.000 0.000 0.676 0.142 0.182 FALSE -0.28 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus 27371789 27374743 GTEx Hippocampus Marginal Yes 27371789-27374743 2.790355e-10 FALSE
Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000096654.11.wgt.RDat Brain_Caudate_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia/Brain_Caudate_basal_ganglia.ENSG00000096654.11.wgt.RDat ZNF184 6 0.11600 rs6938943 -6.310 rs7509 0.072807 4.25 -4.56300 433 433 blup 0.080900 3.39e-04 -6.325200 2.53e-10 0.000 0.000 0.231 0.114 0.655 TRUE 0.53 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Caudate_basal_ganglia 27418522 27440897 GTEx Caudate Joint No 27418522-27440897 2.790355e-10 FALSE
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000096654.11.wgt.RDat Brain_Hypothalamus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000096654.11.wgt.RDat ZNF184 6 0.27900 rs6938943 -6.310 rs13207082 0.140380 4.83 -5.23300 434 18 enet 0.139570 4.38e-05 -4.952200 7.34e-07 0.000 0.000 0.174 0.185 0.641 FALSE 0.66 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus 27418522 27440897 GTEx Hypothalamus Marginal No 27418522-27440897 2.790355e-10 FALSE
NTR.BLOOD.RNAARR/NTR.HIST1H4L.wgt.RDat NTR.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR/NTR.BLOOD.RNAARR/NTR.HIST1H4L.wgt.RDat HIST1H4L 6 0.02310 rs853676 -6.390 rs13218875 0.014479 -5.55 -5.43800 470 470 bslmm 0.013000 3.30e-05 4.870800 1.11e-06 0.000 0.000 0.001 0.162 0.838 FALSE -0.78 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR 27840926 27841289 NTR Blood Marginal No 27840926-27841289 1.658858e-10 TRUE
PEC_TWAS_weights/ENSG00000219891.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000219891.wgt.RDat ZSCAN12P1 6 0.06139 rs6905391 -6.420 rs144436694 0.010266 4.94 4.09400 671 671 bslmm 0.030670 8.82e-11 6.268010 3.66e-10 0.000 0.000 0.056 0.626 0.318 FALSE -0.49 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 28058932 28061442 PsychENCODE Joint No 28058932-28061442 1.362743e-10 FALSE
Whole_Blood/Whole_Blood.ENSG00000219891.2.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000219891.2.wgt.RDat ZSCAN12P1 6 0.03660 rs853676 -6.390 rs1225591 0.012007 4.01 -4.93300 481 481 blup 0.013440 1.48e-02 -4.936930 7.94e-07 0.000 0.000 0.388 0.365 0.247 FALSE 0.64 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 28058932 28061442 GTEx Whole Blood Marginal No 28058932-28061442 1.658858e-10 FALSE
Brain_Hippocampus/Brain_Hippocampus.ENSG00000219392.1.wgt.RDat Brain_Hippocampus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000219392.1.wgt.RDat RP1-265C24.5 6 0.21100 rs853676 -6.390 rs203888 0.141810 -5.09 -3.81500 476 33 enet 0.179240 2.42e-06 5.532000 3.16e-08 0.000 0.000 0.009 0.143 0.847 FALSE -0.73 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus 28083406 28084329 GTEx Hippocampus Marginal No 28083406-28084329 1.658858e-10 TRUE
YFS.BLOOD.RNAARR/YFS.ZSCAN16.wgt.RDat YFS.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.ZSCAN16.wgt.RDat ZSCAN16 6 0.01350 rs853676 -6.390 rs853685 0.005153 4.29 -6.29200 417 417 blup 0.014070 1.40e-05 -6.109000 1.00e-09 0.000 0.000 0.013 0.054 0.933 FALSE 0.80 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR 28092338 28097860 YFS Blood Marginal No 28092338-28097860 1.658858e-10 TRUE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000137185.7.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000137185.7.wgt.RDat ZSCAN9 6 0.12000 rs853676 -6.390 rs13197574 0.032570 4.17 -5.61800 443 443 blup 0.040600 7.15e-03 -5.307800 1.11e-07 0.000 0.000 0.238 0.147 0.615 FALSE 0.73 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 28192664 28201260 GTEx Cerebellum Joint No 28192664-28201260 1.658858e-10 FALSE
Brain_Hippocampus/Brain_Hippocampus.ENSG00000137185.7.wgt.RDat Brain_Hippocampus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000137185.7.wgt.RDat ZSCAN9 6 0.14200 rs853676 -6.390 rs17750424 0.082780 4.19 -5.43700 443 28 enet 0.103880 3.50e-04 -6.017000 1.77e-09 0.000 0.000 0.080 0.053 0.866 FALSE 0.65 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus 28192664 28201260 GTEx Hippocampus Marginal No 28192664-28201260 1.658858e-10 TRUE
Pituitary/Pituitary.ENSG00000137185.7.wgt.RDat Pituitary /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000137185.7.wgt.RDat ZSCAN9 6 0.20000 rs853676 -6.390 rs13197574 0.039090 4.35 -5.61800 443 22 enet 0.062970 9.22e-04 -6.159020 7.32e-10 0.000 0.000 0.169 0.191 0.640 FALSE 0.70 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary 28192664 28201260 GTEx Pituitary Marginal No 28192664-28201260 1.658858e-10 FALSE
PEC_TWAS_weights/ENSG00000189134.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000189134.wgt.RDat NKAPL 6 0.02766 rs6905391 -6.420 rs112863641 0.008266 4.31 3.55700 621 621 bslmm 0.007610 8.74e-04 5.002860 5.65e-07 0.000 0.000 0.627 0.332 0.041 FALSE -0.45 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 28227098 28228736 PsychENCODE Marginal No 28227098-28228736 1.362743e-10 FALSE
Adrenal_Gland/Adrenal_Gland.ENSG00000197062.7.wgt.RDat Adrenal_Gland /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000197062.7.wgt.RDat RP5-874C20.3 6 0.18070 rs853676 -6.390 rs1778508 0.040376 -4.63 -5.77400 426 426 blup 0.103630 8.80e-06 5.094600 3.49e-07 0.000 0.000 0.031 0.146 0.822 FALSE -0.72 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland 28234788 28245974 GTEx Adrenal Gland Marginal No 28234788-28245974 1.658858e-10 TRUE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000197062.7.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000197062.7.wgt.RDat RP5-874C20.3 6 0.47500 rs853676 -6.390 rs213237 0.240770 -6.61 -2.86200 425 425 blup 0.314800 2.75e-14 5.062800 4.13e-07 0.000 0.000 0.000 0.756 0.244 FALSE -0.80 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 28234788 28245974 GTEx Cerebellum Marginal No 28234788-28245974 1.658858e-10 FALSE
Brain_Hippocampus/Brain_Hippocampus.ENSG00000197062.7.wgt.RDat Brain_Hippocampus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000197062.7.wgt.RDat RP5-874C20.3 6 0.27000 rs853676 -6.390 rs16894095 0.072900 -4.28 -2.29100 425 425 blup 0.114230 1.80e-04 5.198000 2.01e-07 0.000 0.000 0.172 0.174 0.654 FALSE -0.69 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus 28234788 28245974 GTEx Hippocampus Marginal No 28234788-28245974 1.658858e-10 FALSE
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000197062.7.wgt.RDat Brain_Putamen_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000197062.7.wgt.RDat RP5-874C20.3 6 0.33900 rs853676 -6.390 rs13408 0.035181 -4.74 -3.40000 425 13 enet 0.119100 1.31e-04 5.739000 9.52e-09 0.000 0.000 0.018 0.051 0.931 FALSE -0.84 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia 28234788 28245974 GTEx Putamen Marginal No 28234788-28245974 1.658858e-10 TRUE
Thyroid/Thyroid.ENSG00000197062.7.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000197062.7.wgt.RDat RP5-874C20.3 6 0.22340 rs853676 -6.390 rs13408 0.220783 -9.47 -3.40000 426 10 lasso 0.281390 1.75e-30 5.338400 9.38e-08 0.000 0.000 0.000 0.858 0.141 FALSE -0.82 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 28234788 28245974 GTEx Thyroid Marginal No 28234788-28245974 1.658858e-10 FALSE
Whole_Blood/Whole_Blood.ENSG00000197062.7.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000197062.7.wgt.RDat RP5-874C20.3 6 0.04100 rs853676 -6.390 rs1778508 0.005820 -3.82 -5.77400 426 426 blup 0.008980 3.82e-02 5.662330 1.49e-08 0.000 0.000 0.288 0.107 0.604 FALSE -0.77 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 28234788 28245974 GTEx Whole Blood Marginal No 28234788-28245974 1.658858e-10 FALSE
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000137338.4.wgt.RDat Brain_Cerebellar_Hemisphere /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000137338.4.wgt.RDat PGBD1 6 0.17900 rs853676 -6.390 rs853685 0.067570 4.32 -6.29200 418 6 lasso 0.076220 1.12e-03 -6.313100 2.74e-10 0.000 0.000 0.032 0.017 0.950 TRUE 0.99 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere 28249314 28270326 GTEx Cerebellar Hemisphere Marginal No 28249314-28270326 1.658858e-10 TRUE
Brain_Amygdala/Brain_Amygdala.ENSG00000235109.3.wgt.RDat Brain_Amygdala /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000235109.3.wgt.RDat ZSCAN31 6 0.26700 rs853676 -6.390 rs203876 0.155750 4.56 -5.03400 382 7 enet 0.146500 1.49e-04 -5.084150 3.69e-07 0.000 0.000 0.381 0.404 0.214 FALSE 0.57 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala 28292470 28324048 GTEx Amygdala Marginal No 28292470-28324048 1.658858e-10 FALSE
Brain_Amygdala/Brain_Amygdala.ENSG00000189298.9.wgt.RDat Brain_Amygdala /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000189298.9.wgt.RDat ZKSCAN3 6 0.16200 rs853676 -6.390 rs1233708 0.060030 -3.63 -5.12500 383 383 blup 0.093000 2.37e-03 4.949900 7.43e-07 0.000 0.000 0.777 0.111 0.111 FALSE -0.51 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala 28317691 28336947 GTEx Amygdala Marginal No 28317691-28336947 1.658858e-10 FALSE
Brain_Hippocampus/Brain_Hippocampus.ENSG00000189298.9.wgt.RDat Brain_Hippocampus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus/Brain_Hippocampus/Brain_Hippocampus.ENSG00000189298.9.wgt.RDat ZKSCAN3 6 0.17600 rs853676 -6.390 rs9393909 0.061440 -4.54 -5.08300 385 10 lasso 0.071750 2.71e-03 4.951000 7.37e-07 0.000 0.000 0.389 0.273 0.338 FALSE -0.74 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hippocampus 28317691 28336947 GTEx Hippocampus Marginal No 28317691-28336947 1.658858e-10 FALSE
Thyroid/Thyroid.ENSG00000189298.9.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000189298.9.wgt.RDat ZKSCAN3 6 0.13670 rs853676 -6.390 rs1233708 0.062905 -6.08 -5.12500 385 385 blup 0.102180 3.98e-11 6.093300 1.11e-09 0.000 0.000 0.000 0.086 0.914 FALSE -0.78 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 28317691 28336947 GTEx Thyroid Marginal No 28317691-28336947 1.658858e-10 TRUE
Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000187987.5.wgt.RDat Brain_Hypothalamus /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus/Brain_Hypothalamus/Brain_Hypothalamus.ENSG00000187987.5.wgt.RDat ZSCAN23 6 0.21000 rs853676 -6.390 rs1233708 0.130370 4.79 -5.12500 342 342 blup 0.135790 5.57e-05 -5.777500 7.58e-09 0.000 0.000 0.062 0.179 0.758 FALSE 0.74 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Hypothalamus 28399707 28411279 GTEx Hypothalamus Joint No 28399707-28411279 1.658858e-10 FALSE
Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000187987.5.wgt.RDat Brain_Putamen_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia/Brain_Putamen_basal_ganglia.ENSG00000187987.5.wgt.RDat ZSCAN23 6 0.22900 rs853676 -6.390 rs9468317 0.101459 4.45 -5.08800 342 342 blup 0.140400 3.27e-05 -4.894000 9.90e-07 0.000 0.000 0.239 0.226 0.535 FALSE 0.70 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Putamen_basal_ganglia 28399707 28411279 GTEx Putamen Marginal No 28399707-28411279 1.658858e-10 FALSE
Pituitary/Pituitary.ENSG00000187987.5.wgt.RDat Pituitary /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000187987.5.wgt.RDat ZSCAN23 6 0.22900 rs853676 -6.390 rs916403 0.134260 5.75 -3.46400 342 342 blup 0.179750 2.09e-08 -4.953290 7.30e-07 0.000 0.000 0.002 0.275 0.723 FALSE 0.72 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary 28399707 28411279 GTEx Pituitary Marginal No 28399707-28411279 1.658858e-10 FALSE
Adrenal_Gland/Adrenal_Gland.ENSG00000146112.7.wgt.RDat Adrenal_Gland /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000146112.7.wgt.RDat PPP1R18 6 0.20040 rs3130557 -5.300 rs2233956 0.046697 -3.98 -2.22200 77 77 blup 0.077590 1.17e-04 4.910200 9.10e-07 0.106 0.007 0.130 0.007 0.750 FALSE -0.02 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland 30644166 30655672 GTEx Adrenal Gland Marginal Yes 30644166-30655672 1.158027e-07 FALSE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709391:clu_26190.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709391:clu_26190.wgt.RDat FLOT1 6 0.28430 rs3130557 -5.300 rs3130557 0.298470 11.52 -5.29900 77 3 lasso 0.295823 0.00e+00 -5.299700 1.16e-07 0.000 0.001 0.000 0.000 0.999 FALSE 0.43 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 30695485 30710682 CMC DLPFC Splicing Marginal Yes 30695485-30710682 1.158027e-07 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709924:clu_26190.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30708575:30709924:clu_26190.wgt.RDat FLOT1 6 0.21370 rs3130557 -5.300 rs3130557 0.182560 9.15 -5.29900 77 10 lasso 0.167113 1.71e-19 -5.067100 4.04e-07 0.000 0.001 0.000 0.000 0.999 FALSE 0.43 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 30695485 30710682 CMC DLPFC Splicing Marginal Yes 30695485-30710682 1.158027e-07 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30709110:30709391:clu_26190.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:30709110:30709391:clu_26190.wgt.RDat FLOT1 6 0.14360 rs3130557 -5.300 rs3130557 0.098890 -7.05 -5.29900 77 3 lasso 0.092157 4.05e-11 4.936600 7.95e-07 0.000 0.001 0.000 0.000 0.999 FALSE -0.40 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 30695485 30710682 CMC DLPFC Splicing Marginal Yes 30695485-30710682 1.158027e-07 TRUE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000137312.10.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000137312.10.wgt.RDat FLOT1 6 0.18400 rs3130557 -5.300 rs3130557 0.112620 4.38 -5.29900 77 1 lasso 0.074900 3.62e-04 -5.299000 1.16e-07 0.010 0.001 0.012 0.000 0.976 FALSE 0.42 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 30695486 30710510 GTEx Cerebellum Marginal Yes 30695486-30710510 1.158027e-07 TRUE
Pituitary/Pituitary.ENSG00000137312.10.wgt.RDat Pituitary /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000137312.10.wgt.RDat FLOT1 6 0.22400 rs3130557 -5.300 rs3130557 0.089150 4.23 -5.29900 77 3 lasso 0.065390 7.44e-04 -5.253270 1.49e-07 0.016 0.001 0.020 0.000 0.963 FALSE 0.41 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary 30695486 30710510 GTEx Pituitary Marginal Yes 30695486-30710510 1.158027e-07 TRUE
Thyroid/Thyroid.ENSG00000137312.10.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000137312.10.wgt.RDat FLOT1 6 0.29080 rs3130557 -5.300 rs3130557 0.132032 7.54 -5.29900 77 4 lasso 0.144340 2.47e-15 -5.557400 2.74e-08 0.000 0.001 0.000 0.000 0.999 FALSE 0.44 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 30695486 30710510 GTEx Thyroid Marginal Yes 30695486-30710510 1.158027e-07 TRUE
Brain_Cortex/Brain_Cortex.ENSG00000137411.12.wgt.RDat Brain_Cortex /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000137411.12.wgt.RDat VARS2 6 0.37500 rs3130557 -5.300 rs1811197 0.114080 -4.02 -4.98000 95 5 lasso 0.098840 1.21e-04 5.922000 3.18e-09 0.105 0.004 0.158 0.005 0.727 FALSE -0.66 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex 30881982 30894236 GTEx Cortex Marginal Yes 30881982-30894236 1.158027e-07 FALSE
Whole_Blood/Whole_Blood.ENSG00000137411.12.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000137411.12.wgt.RDat VARS2 6 0.17210 rs3130557 -5.300 rs3130557 0.046674 -4.37 -5.29900 95 95 blup 0.039720 7.00e-05 6.323130 2.56e-10 0.005 0.001 0.007 0.000 0.986 FALSE -0.60 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 30881982 30894236 GTEx Whole Blood Marginal Yes 30881982-30894236 1.158027e-07 TRUE
Pituitary/Pituitary.ENSG00000231402.1.wgt.RDat Pituitary /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000231402.1.wgt.RDat WASF5P 6 0.50500 rs3130557 -5.300 rs2523578 0.236690 6.26 -4.44300 207 207 blup 0.233340 1.02e-10 -5.156240 2.52e-07 0.000 0.046 0.000 0.091 0.862 FALSE 0.54 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary 31255287 31256741 GTEx Pituitary Marginal Yes 31255287-31256741 1.158027e-07 TRUE
Thyroid/Thyroid.ENSG00000206337.6.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000206337.6.wgt.RDat HCP5 6 0.09580 rs3130557 -5.300 rs3094005 0.059439 -5.62 -5.01000 243 243 blup 0.051290 2.99e-06 6.400800 1.55e-10 0.000 0.008 0.000 0.015 0.976 TRUE -0.58 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 31368479 31445283 GTEx Thyroid Joint Yes 31368479-31445283 1.158027e-07 TRUE
Thyroid/Thyroid.ENSG00000204516.5.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000204516.5.wgt.RDat MICB 6 0.46800 rs3130557 -5.300 rs2534671 0.118617 8.29 -4.63600 280 27 enet 0.322490 0.00e+00 -5.557000 2.74e-08 0.000 0.047 0.000 0.094 0.859 TRUE 0.20 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 31462658 31478901 GTEx Thyroid Joint Yes 31462658-31478901 1.158027e-07 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:31619553:31620177:clu_26248.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:31619553:31620177:clu_26248.wgt.RDat BAG6 6 0.09910 rs3094005 -5.010 rs2239689 0.042150 -5.27 2.38100 245 245 blup 0.052463 6.57e-07 -5.580000 2.40e-08 0.008 0.408 0.006 0.319 0.259 FALSE 0.40 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 31606805 31620482 CMC DLPFC Splicing Joint Yes 31606805-31620482 5.443004e-07 FALSE
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000213722.4.wgt.RDat Brain_Frontal_Cortex_BA9 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000213722.4.wgt.RDat DDAH2 6 0.32300 rs3094005 -5.010 rs707938 0.119210 -4.28 -3.30800 242 242 blup 0.094280 4.46e-04 5.409500 6.32e-08 0.331 0.042 0.258 0.033 0.336 FALSE -0.30 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 31694815 31698357 GTEx Frontal Cortex Marginal Yes 31694815-31698357 5.443004e-07 FALSE
CMC.BRAIN.RNASEQ/CMC.DDAH2.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.DDAH2.wgt.RDat DDAH2 6 0.07420 rs3094005 -5.010 rs1144708 0.042170 -5.86 -4.46600 245 245 blup 0.053500 3.92e-07 5.344500 9.07e-08 0.000 0.051 0.000 0.039 0.909 FALSE -0.46 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 31694816 31698039 CMC DLPFC Marginal Yes 31694816-31698039 5.443004e-07 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:99831006:99831574:clu_27287.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr6:99831006:99831574:clu_27287.wgt.RDat COQ3 6 0.04000 rs10457592 5.230 rs2029965 0.011000 -3.35 -1.91300 474 474 bslmm 0.007760 3.54e-02 5.146560 2.65e-07 0.324 0.015 0.598 0.029 0.034 TRUE 0.22 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 99817347 99842082 CMC DLPFC Splicing Joint Yes 99817347-99842082 1.695100e-07 FALSE
CMC.BRAIN.RNASEQ/CMC.LIN28B.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.LIN28B.wgt.RDat LIN28B 6 0.04180 rs1475120 -5.300 rs4946651 0.038000 5.19 -5.26100 434 9 lasso 0.034788 3.91e-05 -5.232050 1.68e-07 0.000 0.001 0.000 0.008 0.990 FALSE 0.97 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 105404922 105531207 CMC DLPFC Marginal Yes 105404922-105531207 1.158027e-07 TRUE
PEC_TWAS_weights/ENSG00000187772.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000187772.wgt.RDat LIN28B 6 0.06906 rs370771 -5.340 rs13203645 0.044900 -8.64 4.94900 1270 18 enet 0.061400 3.70e-20 -5.105689 3.30e-07 0.000 0.005 0.000 0.052 0.943 FALSE 0.73 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 105404923 105531207 PsychENCODE Marginal Yes 105404923-105531207 9.294658e-08 TRUE
Brain_Amygdala/Brain_Amygdala.ENSG00000203808.6.wgt.RDat Brain_Amygdala /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000203808.6.wgt.RDat BVES-AS1 6 0.34900 rs1475120 -5.300 rs2153127 0.104990 -3.89 4.60700 377 7 lasso 0.093320 2.33e-03 -5.578300 2.43e-08 0.061 0.007 0.373 0.045 0.514 TRUE 0.81 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala 105584224 105617820 GTEx Amygdala Joint Yes 105584224-105617820 1.158027e-07 FALSE
PEC_TWAS_weights/ENSG00000106460.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000106460.wgt.RDat TMEM106B 7 0.06497 rs3815535 5.440 rs3800847 0.039600 -7.91 5.36300 2571 6 lasso 0.041800 3.68e-14 -5.790690 7.01e-09 0.000 0.001 0.000 0.054 0.945 TRUE -0.95 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 12250867 12282993 PsychENCODE Joint Yes 12250867-12282993 5.328057e-08 TRUE
Adrenal_Gland/Adrenal_Gland.ENSG00000106460.14.wgt.RDat Adrenal_Gland /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000106460.14.wgt.RDat TMEM106B 7 0.22000 rs1990622 5.420 rs6460900 0.126809 4.93 5.28500 672 3 lasso 0.084100 6.14e-05 5.505026 3.69e-08 0.000 0.001 0.003 0.009 0.987 FALSE 0.98 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland 12250867 12282993 GTEx Adrenal Gland Marginal Yes 12250867-12282993 5.959904e-08 TRUE
Whole_Blood/Whole_Blood.ENSG00000106460.14.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000106460.14.wgt.RDat TMEM106B 7 0.38280 rs1990622 5.420 rs6460900 0.142440 7.73 5.28500 672 41 enet 0.201363 7.48e-20 5.531000 3.18e-08 0.000 0.001 0.000 0.008 0.991 FALSE 0.82 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 12250867 12282993 GTEx Whole Blood Marginal Yes 12250867-12282993 5.959904e-08 TRUE
YFS.BLOOD.RNAARR/YFS.TMEM106B.wgt.RDat YFS.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.TMEM106B.wgt.RDat TMEM106B 7 0.07763 rs1990622 5.420 rs5011432 0.055000 8.73 5.35000 677 7 lasso 0.055160 1.65e-17 5.373600 7.72e-08 0.000 0.001 0.000 0.007 0.993 FALSE 0.95 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR 12250867 12276886 YFS Blood Marginal Yes 12250867-12276886 5.959904e-08 TRUE
Pituitary/Pituitary.ENSG00000070882.8.wgt.RDat Pituitary /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000070882.8.wgt.RDat OSBPL3 7 0.32980 rs10486432 4.960 rs8180777 0.044100 -4.25 0.96600 610 610 blup 0.129980 2.25e-06 -5.622890 1.88e-08 0.090 0.040 0.062 0.027 0.780 TRUE -0.53 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary 24836158 25021253 GTEx Pituitary Joint Yes 24836158-25021253 7.049318e-07 FALSE
CMC.BRAIN.RNASEQ/CMC.PXDNL.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.PXDNL.wgt.RDat PXDNL 8 0.05330 rs12548147 5.250 rs7008730 -0.000193 3.73 3.26800 586 586 blup 0.015150 5.06e-03 5.887460 3.92e-09 0.090 0.019 0.318 0.065 0.508 TRUE 0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 52232136 52722005 CMC DLPFC Joint Yes 52232136-52722005 1.520992e-07 FALSE
Thyroid/Thyroid.ENSG00000251396.2.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000251396.2.wgt.RDat RP11-163N6.2 8 0.04980 rs618190 5.020 rs597123 0.011932 -4.38 3.33300 402 402 blup 0.013830 1.08e-02 -5.336530 9.47e-08 0.084 0.162 0.118 0.228 0.408 TRUE -0.74 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 61297147 61429354 GTEx Thyroid Joint Yes 61297147-61429354 5.167148e-07 FALSE
PEC_TWAS_weights/ENSG00000234881.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000234881.wgt.RDat PIGFP2 9 0.02070 rs7029033 5.560 rs667138 0.003030 -4.03 4.58000 1396 1396 bslmm 0.001730 6.99e-02 -5.305600 1.12e-07 0.017 0.004 0.603 0.126 0.250 TRUE -0.50 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 126605315 126605965 PsychENCODE Joint No 126605315-126605965 2.697747e-08 FALSE
Adrenal_Gland/Adrenal_Gland.ENSG00000149115.9.wgt.RDat Adrenal_Gland /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000149115.9.wgt.RDat TNKS1BP1 11 0.17620 rs11607056 4.970 rs11228997 0.004394 4.05 3.09700 412 412 blup 0.092060 2.79e-05 4.922610 8.54e-07 0.080 0.025 0.107 0.032 0.756 TRUE 0.49 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland 57067112 57092426 GTEx Adrenal Gland Joint Yes 57067112-57092426 6.695290e-07 FALSE
Thyroid/Thyroid.ENSG00000254602.1.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000254602.1.wgt.RDat AP000662.4 11 0.24480 rs11607056 4.970 rs11607122 0.095400 -6.95 3.59200 396 46 enet 0.153544 2.82e-16 -4.980256 6.35e-07 0.000 0.136 0.000 0.255 0.610 FALSE -0.62 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 57405497 57420263 GTEx Thyroid Marginal Yes 57405497-57420263 6.695290e-07 FALSE
Whole_Blood/Whole_Blood.ENSG00000172409.5.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000172409.5.wgt.RDat CLP1 11 0.04780 rs11607056 4.970 rs9420 0.035200 4.80 4.68900 390 4 lasso 0.032330 3.10e-04 5.195860 2.04e-07 0.001 0.008 0.002 0.015 0.974 TRUE 0.88 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 57424488 57429340 GTEx Whole Blood Joint Yes 57424488-57429340 6.695290e-07 TRUE
PEC_TWAS_weights/ENSG00000134825.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000134825.wgt.RDat TMEM258 11 0.07329 rs174594 5.060 rs174536 0.012300 5.95 4.36600 1206 9 enet 0.012700 2.35e-05 5.021730 5.12e-07 0.000 0.049 0.000 0.041 0.910 TRUE 0.73 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 61535973 61560274 PsychENCODE Joint Yes 61535973-61560274 4.192565e-07 TRUE
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000149295.9.wgt.RDat Brain_Frontal_Cortex_BA9 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000149295.9.wgt.RDat DRD2 11 0.43370 rs2514218 -5.030 rs4319541 -0.004170 -3.58 2.09000 526 526 blup 0.100150 2.99e-04 -5.073787 3.90e-07 0.366 0.032 0.515 0.045 0.042 TRUE 0.00 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 113280318 113346111 GTEx Frontal Cortex Joint Yes 113280318-113346111 4.904798e-07 FALSE
CMC.BRAIN.RNASEQ/CMC.OLFM4.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.OLFM4.wgt.RDat OLFM4 13 0.03980 rs12552 8.890 rs1535576 0.027299 -3.86 -3.38200 507 507 blup 0.008642 2.69e-02 5.091290 3.56e-07 0.000 0.000 0.865 0.089 0.046 TRUE 0.42 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 53602875 53626196 CMC DLPFC Joint No 53602875-53626196 6.110891e-19 FALSE
Thyroid/Thyroid.ENSG00000258636.1.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000258636.1.wgt.RDat CTD-2298J14.2 14 0.11180 rs1950829 5.940 rs12431444 0.073500 -5.91 5.69800 353 3 lasso 0.057336 8.03e-07 -5.678860 1.36e-08 0.000 0.000 0.000 0.022 0.978 TRUE -0.87 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 42057064 42074059 GTEx Thyroid Joint No 42057064-42074059 2.850221e-09 TRUE
Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000165379.9.wgt.RDat Brain_Cerebellar_Hemisphere /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere/Brain_Cerebellar_Hemisphere.ENSG00000165379.9.wgt.RDat LRFN5 14 0.43200 rs1950829 5.940 rs11157247 0.204000 6.15 4.86000 353 353 blup 0.310050 1.13e-11 5.423400 5.85e-08 0.000 0.000 0.000 0.029 0.971 FALSE 0.78 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellar_Hemisphere 42076773 42373752 GTEx Cerebellar Hemisphere Marginal No 42076773-42373752 2.850221e-09 TRUE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000165379.9.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000165379.9.wgt.RDat LRFN5 14 0.41100 rs1950829 5.940 rs8008204 0.152180 5.94 5.09100 353 6 lasso 0.195060 6.59e-09 5.597540 2.17e-08 0.000 0.000 0.000 0.041 0.959 FALSE 0.89 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 42076773 42373752 GTEx Cerebellum Marginal No 42076773-42373752 2.850221e-09 TRUE
Thyroid/Thyroid.ENSG00000050130.13.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000050130.13.wgt.RDat JKAMP 14 0.14070 rs12893956 5.080 rs1952039 0.037700 -4.89 5.02000 460 460 blup 0.056384 9.88e-07 -5.125100 2.97e-07 0.001 0.004 0.004 0.022 0.969 FALSE -0.62 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 59951161 59971429 GTEx Thyroid Marginal Yes 59951161-59971429 3.774349e-07 TRUE
Thyroid/Thyroid.ENSG00000151838.7.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000151838.7.wgt.RDat CCDC175 14 0.13690 rs12893956 5.080 rs2182140 0.076100 -6.21 4.97200 424 424 blup 0.072917 2.66e-08 -5.478850 4.28e-08 0.000 0.004 0.000 0.018 0.979 TRUE -0.64 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 59971779 60043549 GTEx Thyroid Joint Yes 59971779-60043549 3.774349e-07 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:60074210:60097164:clu_16682.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:60074210:60097164:clu_16682.wgt.RDat RTN1 14 0.04170 rs12893956 5.080 rs12587247 0.025600 -4.59 4.72000 420 420 blup 0.019700 1.78e-03 -4.874920 1.09e-06 0.001 0.006 0.007 0.033 0.953 FALSE -0.85 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 60062693 60337557 CMC DLPFC Splicing Marginal Yes 60062693-60337557 3.774349e-07 TRUE
Thyroid/Thyroid.ENSG00000139970.12.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000139970.12.wgt.RDat RTN1 14 0.25130 rs12893956 5.080 rs1952039 0.154000 -8.02 5.02000 358 6 lasso 0.152868 3.31e-16 -5.348450 8.87e-08 0.000 0.003 0.000 0.016 0.981 FALSE -0.77 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 60062695 60337684 GTEx Thyroid Marginal Yes 60062695-60337684 3.774349e-07 TRUE
NTR.BLOOD.RNAARR/NTR.SYNE2.wgt.RDat NTR.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR/NTR.BLOOD.RNAARR/NTR.SYNE2.wgt.RDat SYNE2 14 0.07960 rs915057 -6.150 rs3020445 0.077706 -10.10 -5.71300 510 5 lasso 0.080663 9.92e-25 5.609528 2.03e-08 0.000 0.000 0.000 0.016 0.984 FALSE -0.94 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/NTR.BLOOD.RNAARR 64319682 64693151 NTR Blood Marginal No 64319682-64693151 7.748295e-10 TRUE
Pituitary/Pituitary.ENSG00000140009.14.wgt.RDat Pituitary /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary/Pituitary/Pituitary.ENSG00000140009.14.wgt.RDat ESR2 14 0.21120 rs915057 -6.150 rs1256033 -0.005360 4.10 -5.46600 456 19 enet 0.028550 1.97e-02 -5.982300 2.20e-09 0.000 0.000 0.113 0.026 0.860 TRUE 0.70 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Pituitary 64550950 64770377 GTEx Pituitary Joint No 64550950-64770377 7.748295e-10 TRUE
Whole_Blood/Whole_Blood.ENSG00000140009.14.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000140009.14.wgt.RDat ESR2 14 0.10120 rs915057 -6.150 rs6573553 0.050018 5.67 -5.46200 457 9 enet 0.067100 2.81e-07 -5.655371 1.56e-08 0.000 0.000 0.000 0.014 0.986 FALSE 0.91 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 64550950 64770377 GTEx Whole Blood Marginal No 64550950-64770377 7.748295e-10 TRUE
PEC_TWAS_weights/ENSG00000119682.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000119682.wgt.RDat AREL1 14 0.17261 rs2005864 5.800 rs11621186 0.138000 14.00 -4.78500 1130 1130 bslmm 0.163000 0.00e+00 -5.015110 5.30e-07 0.000 0.002 0.000 0.216 0.782 FALSE -0.77 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 75120140 75179818 PsychENCODE Marginal No 75120140-75179818 6.631492e-09 FALSE
Thyroid/Thyroid.ENSG00000119608.8.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000119608.8.wgt.RDat PROX2 14 0.06530 rs1045430 -5.710 rs2300596 0.011200 -4.49 5.58500 368 13 enet 0.011563 1.80e-02 -5.758100 8.51e-09 0.000 0.000 0.017 0.020 0.962 TRUE -0.89 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 75319736 75330537 GTEx Thyroid Joint No 75319736-75330537 1.129762e-08 TRUE
CMC.BRAIN.RNASEQ/CMC.DLST.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.DLST.wgt.RDat DLST 14 0.09270 rs1045430 -5.710 rs2111705 0.105642 -7.28 -4.40600 386 2 lasso 0.108580 3.96e-13 4.981400 6.31e-07 0.000 0.001 0.000 0.047 0.952 FALSE 0.89 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 75348593 75370450 CMC DLPFC Marginal No 75348593-75370450 1.129762e-08 TRUE
PEC_TWAS_weights/ENSG00000119689.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000119689.wgt.RDat DLST 14 0.04731 rs2005864 5.800 rs8010840 0.024100 6.34 5.61000 1076 31 enet 0.032600 2.24e-11 5.089700 3.59e-07 0.000 0.000 0.000 0.023 0.977 FALSE 0.80 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 75348594 75370448 PsychENCODE Marginal No 75348594-75370448 6.631492e-09 TRUE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:75375893:75377951:clu_16995.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr14:75375893:75377951:clu_16995.wgt.RDat RPS6KL1 14 0.08080 rs1045430 -5.710 rs2359239 0.026700 4.40 -4.17600 382 2 lasso 0.027820 2.48e-04 -5.023810 5.07e-07 0.003 0.001 0.205 0.082 0.708 FALSE -0.57 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 75370656 75389188 CMC DLPFC Splicing Marginal No 75370656-75389188 1.129762e-08 FALSE
PEC_TWAS_weights/ENSG00000198208.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000198208.wgt.RDat RPS6KL1 14 0.01434 rs2005864 5.800 rs12888998 -0.000719 -3.58 5.27000 1056 1056 bslmm 0.001410 9.08e-02 -4.952550 7.32e-07 0.002 0.000 0.176 0.031 0.791 FALSE -0.87 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 75370657 75390099 PsychENCODE Marginal No 75370657-75390099 6.631492e-09 FALSE
PEC_TWAS_weights/ENSG00000244691.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000244691.wgt.RDat RP11-600F24.2 14 0.01456 rs10149470 -5.930 rs2403193 0.000357 -3.98 -5.16700 1598 2 lasso 0.003470 1.81e-02 5.185660 2.15e-07 0.007 0.002 0.552 0.202 0.238 FALSE -0.64 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 103878456 103879098 PsychENCODE Marginal No 103878456-103879098 3.029347e-09 FALSE
YFS.BLOOD.RNAARR/YFS.CKB.wgt.RDat YFS.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.CKB.wgt.RDat CKB 14 0.02530 rs2296483 -5.370 rs10129426 0.023900 -5.66 -5.33600 349 4 lasso 0.018640 6.64e-07 5.346000 8.99e-08 0.000 0.001 0.000 0.005 0.995 FALSE -0.97 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR 103985996 103989448 YFS Blood Marginal Yes 103985996-103989448 7.873664e-08 TRUE
CMC.BRAIN.RNASEQ/CMC.TRMT61A.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.TRMT61A.wgt.RDat TRMT61A 14 0.06010 rs2296483 -5.370 rs942866 0.025997 -4.79 -5.21700 349 3 lasso 0.042040 6.61e-06 5.051300 4.39e-07 0.001 0.004 0.004 0.030 0.961 FALSE -0.57 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 103995508 104003410 CMC DLPFC Marginal Yes 103995508-104003410 7.873664e-08 TRUE
Whole_Blood/Whole_Blood.ENSG00000166166.8.wgt.RDat Whole_Blood /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood/Whole_Blood/Whole_Blood.ENSG00000166166.8.wgt.RDat TRMT61A 14 0.07160 rs2296483 -5.370 rs7154572 0.017206 -4.48 -3.40800 354 3 lasso 0.034600 1.97e-04 4.977593 6.44e-07 0.006 0.010 0.049 0.081 0.854 FALSE -0.64 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Whole_Blood 103995521 104003410 GTEx Whole Blood Marginal Yes 103995521-104003410 7.873664e-08 TRUE
Thyroid/Thyroid.ENSG00000258851.1.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000258851.1.wgt.RDat RP11-894P9.2 14 0.08430 rs2296483 -5.370 rs10129426 0.069700 5.42 -5.33600 361 5 lasso 0.064092 1.84e-07 -5.462560 4.69e-08 0.000 0.001 0.000 0.005 0.994 TRUE 0.94 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 104019758 104028214 GTEx Thyroid Joint Yes 104019758-104028214 7.873664e-08 TRUE
PEC_TWAS_weights/ENSG00000270108.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000270108.wgt.RDat RP11-73M18.6 14 0.03617 rs10149470 -5.930 rs4906358 0.017500 -5.52 -4.62700 1758 1758 bslmm 0.017700 7.13e-07 5.031320 4.87e-07 0.000 0.005 0.001 0.413 0.581 FALSE -0.63 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 104153913 104154464 PsychENCODE Marginal No 104153913-104154464 3.029347e-09 FALSE
PEC_TWAS_weights/ENSG00000269940.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000269940.wgt.RDat RP11-73M18.7 14 0.06550 rs10149470 -5.930 esv3635603 0.035600 -8.21 -4.55400 1755 13 lasso 0.055600 2.22e-18 4.856130 1.20e-06 0.000 0.006 0.000 0.513 0.480 FALSE -0.65 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 104160897 104161507 PsychENCODE Marginal No 104160897-104161507 3.029347e-09 FALSE
Brain_Amygdala/Brain_Amygdala.ENSG00000269958.1.wgt.RDat Brain_Amygdala /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala/Brain_Amygdala/Brain_Amygdala.ENSG00000269958.1.wgt.RDat RP11-73M18.8 14 0.30000 rs2296483 -5.370 rs2296483 0.181510 -4.31 -5.37200 353 5 lasso 0.111880 9.04e-04 5.142000 2.72e-07 0.010 0.002 0.082 0.019 0.887 FALSE -0.70 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Amygdala 104162690 104163500 GTEx Amygdala Marginal Yes 104162690-104163500 7.873664e-08 TRUE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000224997.1.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000224997.1.wgt.RDat AL049840.1 14 0.27100 rs2296483 -5.370 rs861544 0.052800 -4.75 -4.50100 351 351 blup 0.082090 1.94e-04 5.029540 4.92e-07 0.001 0.003 0.008 0.026 0.962 FALSE -0.60 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 104177607 104179149 GTEx Cerebellum Marginal Yes 104177607-104179149 7.873664e-08 TRUE
Brain_Cortex/Brain_Cortex.ENSG00000224997.1.wgt.RDat Brain_Cortex /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000224997.1.wgt.RDat AL049840.1 14 0.26800 rs2296483 -5.370 rs11625397 0.182495 -5.06 -5.13000 350 4 lasso 0.160000 8.82e-07 5.143620 2.69e-07 0.001 0.002 0.007 0.012 0.979 FALSE -0.72 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex 104177607 104179149 GTEx Cortex Marginal Yes 104177607-104179149 7.873664e-08 TRUE
Brain_Cortex/Brain_Cortex.ENSG00000269963.1.wgt.RDat Brain_Cortex /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex/Brain_Cortex/Brain_Cortex.ENSG00000269963.1.wgt.RDat RP11-73M18.9 14 0.24800 rs2296483 -5.370 rs11625397 0.198292 -5.37 -5.13000 349 4 lasso 0.186000 1.06e-07 4.977330 6.45e-07 0.000 0.002 0.001 0.013 0.984 FALSE -0.69 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cortex 104179904 104180441 GTEx Cortex Marginal Yes 104179904-104180441 7.873664e-08 TRUE
PEC_TWAS_weights/ENSG00000269963.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000269963.wgt.RDat RP11-73M18.9 14 0.01265 rs10149470 -5.930 rs3759586 0.014300 -4.83 -4.84900 1749 2 lasso 0.010900 8.45e-05 4.830100 1.36e-06 0.001 0.005 0.049 0.425 0.520 FALSE -0.62 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 104179904 104180586 PsychENCODE Marginal No 104179904-104180586 3.029347e-09 FALSE
PEC_TWAS_weights/ENSG00000118557.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000118557.wgt.RDat PMFBP1 16 0.02226 rs9936642 5.460 rs8043722 0.000245 -4.07 4.80200 1349 1349 blup 0.004720 7.14e-03 -5.160620 2.46e-07 0.013 0.005 0.174 0.069 0.738 TRUE -0.70 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 72146056 72210777 PsychENCODE Joint No 72146056-72210777 4.761346e-08 FALSE
Adrenal_Gland/Adrenal_Gland.ENSG00000196535.10.wgt.RDat Adrenal_Gland /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000196535.10.wgt.RDat MYO18A 17 0.37380 rs8066520 5.060 rs4795491 0.123317 -5.45 4.34000 337 337 blup 0.167285 1.26e-08 -5.128570 2.92e-07 0.002 0.040 0.001 0.019 0.937 FALSE -0.62 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland 27400528 27507430 GTEx Adrenal Gland Marginal Yes 27400528-27507430 4.192565e-07 TRUE
Adrenal_Gland/Adrenal_Gland.ENSG00000221995.4.wgt.RDat Adrenal_Gland /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland/Adrenal_Gland/Adrenal_Gland.ENSG00000221995.4.wgt.RDat TIAF1 17 0.30370 rs8066520 5.060 rs869718 0.085762 -5.08 3.64100 324 324 blup 0.095323 2.01e-05 -5.361200 8.27e-08 0.016 0.111 0.008 0.055 0.810 TRUE -0.46 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Adrenal_Gland 27401933 27405875 GTEx Adrenal Gland Joint Yes 27401933-27405875 4.192565e-07 TRUE
PEC_TWAS_weights/ENSG00000264754.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000264754.wgt.RDat CTD-2653B5.1 17 0.06754 rs60856912 4.180 rs8069128 0.025500 -6.44 -3.82146 1225 1225 bslmm 0.023300 1.44e-08 5.105730 3.30e-07 0.000 0.441 0.000 0.028 0.531 TRUE 0.13 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 65520597 65521538 PsychENCODE Joint Yes 65520597-65521538 2.915091e-05 FALSE
PEC_TWAS_weights/ENSG00000041353.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000041353.wgt.RDat RAB27B 18 0.13240 rs1262464 -5.780 rs12970424 0.057700 -9.79 -4.57400 1433 1433 blup 0.063700 7.52e-21 5.012900 5.36e-07 0.000 0.015 0.000 0.328 0.657 TRUE 0.11 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 52385091 52562747 PsychENCODE Joint Yes 52385091-52562747 7.470063e-09 FALSE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr18:52385372:52544798:clu_20555.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr18:52385372:52544798:clu_20555.wgt.RDat RAB27B 18 0.18080 rs11875348 -5.120 rs2871673 0.173248 -8.88 -4.77300 368 9 lasso 0.159800 1.20e-18 4.843190 1.28e-06 0.000 0.016 0.000 0.038 0.945 FALSE 0.11 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 52495707 52562747 CMC DLPFC Splicing Marginal Yes 52495707-52562747 3.055357e-07 TRUE
CMC.BRAIN.RNASEQ/CMC.DDX27.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.DDX27.wgt.RDat DDX27 20 0.04460 rs11697370 -4.640 rs7266044 0.038362 -5.08 -3.95500 441 11 enet 0.069662 7.29e-09 4.836260 1.32e-06 0.003 0.067 0.001 0.029 0.900 TRUE -0.78 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 47835831 47860614 CMC DLPFC Joint Yes 47835831-47860614 3.484092e-06 TRUE
Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000100372.10.wgt.RDat Brain_Nucleus_accumbens_basal_ganglia /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia/Brain_Nucleus_accumbens_basal_ganglia.ENSG00000100372.10.wgt.RDat SLC25A17 22 0.13620 rs2179744 5.730 rs13054099 0.098545 4.16 4.32200 287 8 enet 0.049730 6.36e-03 5.076990 3.83e-07 0.007 0.001 0.547 0.097 0.348 FALSE 0.74 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia 41165634 41215403 GTEx Nucleus accumbens Marginal No 41165634-41215403 1.004306e-08 FALSE
Thyroid/Thyroid.ENSG00000100372.10.wgt.RDat Thyroid /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid/Thyroid/Thyroid.ENSG00000100372.10.wgt.RDat SLC25A17 22 0.08390 rs2179744 5.730 rs5758064 0.044400 -5.31 -4.02000 289 289 blup 0.059630 4.87e-07 4.896100 9.78e-07 0.000 0.008 0.020 0.671 0.301 FALSE 0.47 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Thyroid 41165634 41215403 GTEx Thyroid Marginal No 41165634-41215403 1.004306e-08 FALSE
Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000196236.8.wgt.RDat Brain_Frontal_Cortex_BA9 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9/Brain_Frontal_Cortex_BA9.ENSG00000196236.8.wgt.RDat XPNPEP3 22 0.22800 rs2179744 5.730 rs133076 0.006324 3.91 4.53000 282 15 enet 0.088290 6.68e-04 4.951000 7.38e-07 0.009 0.001 0.731 0.091 0.168 FALSE 0.45 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Frontal_Cortex_BA9 41253088 41351450 GTEx Frontal Cortex Marginal No 41253088-41351450 1.004306e-08 FALSE
CMC.BRAIN.RNASEQ/CMC.XPNPEP3.wgt.RDat CMC.BRAIN.RNASEQ /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ/CMC.BRAIN.RNASEQ/CMC.XPNPEP3.wgt.RDat XPNPEP3 22 0.05530 rs2179744 5.730 rs138354 0.035200 -5.57 -4.32500 318 4 lasso 0.044230 3.86e-06 5.110000 3.21e-07 0.000 0.004 0.005 0.358 0.632 FALSE 0.64 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ 41258260 41363888 CMC DLPFC Marginal No 41258260-41363888 1.004306e-08 FALSE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000100393.9.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000100393.9.wgt.RDat EP300 22 0.11400 rs2179744 5.730 rs3171692 0.040045 4.45 4.69400 276 276 blup 0.063473 9.81e-04 5.493900 3.93e-08 0.001 0.001 0.061 0.049 0.888 FALSE 0.85 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 41487790 41576081 GTEx Cerebellum Marginal No 41487790-41576081 1.004306e-08 TRUE
YFS.BLOOD.RNAARR/YFS.EP300.wgt.RDat YFS.BLOOD.RNAARR /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR/YFS.BLOOD.RNAARR/YFS.EP300.wgt.RDat EP300 22 0.04512 rs2179744 5.730 rs139480 0.042424 -7.51 -4.00200 284 284 blup 0.046630 5.18e-15 5.059100 4.21e-07 0.000 0.012 0.000 0.955 0.033 FALSE 0.61 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/YFS.BLOOD.RNAARR 41487790 41576081 YFS Blood Marginal No 41487790-41576081 1.004306e-08 FALSE
CMC.BRAIN.RNASEQ_SPLICING/CMC.chr22:41657584:41664101:clu_21526.wgt.RDat CMC.BRAIN.RNASEQ_SPLICING /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING/CMC.BRAIN.RNASEQ_SPLICING/CMC.chr22:41657584:41664101:clu_21526.wgt.RDat RANGAP1 22 0.02850 rs2179744 5.730 rs5758209 -0.002214 -3.04 -4.88500 251 251 blup 0.003925 9.85e-02 5.240100 1.61e-07 0.010 0.000 0.814 0.028 0.147 FALSE 0.57 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING 41641614 41682216 CMC DLPFC Splicing Marginal No 41641614-41682216 1.004306e-08 FALSE
PEC_TWAS_weights/ENSG00000100401.wgt.RDat PsychENCODE /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights/PEC_TWAS_weights/ENSG00000100401.wgt.RDat RANGAP1 22 0.05133 rs5758265 5.780 rs5751074 0.041600 -7.48 4.72400 1064 4 lasso 0.053336 1.13e-17 -5.575273 2.47e-08 0.000 0.004 0.000 0.705 0.290 FALSE -0.81 /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/PEC_TWAS_weights 41641615 41682255 PsychENCODE Marginal No 41641615-41682255 7.470063e-09 FALSE
Brain_Cerebellum/Brain_Cerebellum.ENSG00000100403.10.wgt.RDat Brain_Cerebellum /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum/Brain_Cerebellum/Brain_Cerebellum.ENSG00000100403.10.wgt.RDat ZC3H7B 22 0.11000 rs2179744 5.730 rs6002271 0.072645 4.09 5.58700 269 4 lasso 0.037831 9.13e-03 5.729100 1.01e-08 0.001 0.000 0.105 0.031 0.862 TRUE 0.93 /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/SNP-weights/Brain_Cerebellum 41697526 41756151 GTEx Cerebellum Joint No 41697526-41756151 1.004306e-08 TRUE

3.7 FOCUS

Merge the relevent FOCUS databases

# Create a list of databases to be merged.
cat << 'EOF' > /scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS_db_list.txt
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Adrenal_Gland.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Amygdala.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Anterior_cingulate_cortex_BA24.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Caudate_basal_ganglia.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Cerebellar_Hemisphere.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Cerebellum.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Cortex.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Frontal_Cortex_BA9.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Hippocampus.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Hypothalamus.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Nucleus_accumbens_basal_ganglia.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Putamen_basal_ganglia.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Spinal_cord_cervical_c-1.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Brain_Substantia_nigra.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/CMC.BRAIN.RNASEQ.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/CMC.BRAIN.RNASEQ_SPLICING.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/NTR.BLOOD.RNAARR.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/PEC_TWAS_weights.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Pituitary.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Thyroid.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/Whole_Blood.db
/scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/SNP-weights/YFS.BLOOD.RNAARR.db
EOF

####
# Merge the databases
####
# I have written a script to do this in R
/users/k1806347/brc_scratch/Software/Rscript.sh /scratch/users/k1806347/Software/MyGit/FOCUS_db_merger/FOCUS_db_merger.r \
--dbs /scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS_db_list.txt \
--out /scratch/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS

# This approach is giving strange results.
# Delete the output

Create FOCUS database for MDD TWAS

# Merging the databases causes some strange warnings
# Use the standard approach for the time being

########
# Import the FUSION SNP-weights to FOCUS format
########

# Import with all tissues in the MDD TWAS combined
# A shell script to do this has been written
mkdir -p /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db

# Fusion weights
sbatch -p brc,shared --mem=10G /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/create_db_fusion.sh

# Onyl continue once the FUSION SNP-weights are in the database
# Psych ENCODE weights
sbatch -p brc,shared --mem=10G /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/create_db_psychENCODE.sh

Run FOCUS

########
# Finemap TWAS associations
########

# Use a threshold that will run FOCUS for all TWAS significant loci (max GWAS.P = 5e-6)
for chr in $(seq 22 22); do
sbatch -p brc,shared --mem=10G /users/k1806347/brc_scratch/Software/focus.sh finemap /users/k1806347/brc_scratch/Data/GWAS_sumstats/Lorenza/DEPR01.focus.sumstats.gz  /scratch/groups/biomarkers-brc-mh/Reference_data/1KG_Phase3/PLINK/EUR/EUR_phase3.MAF_001.chr${chr} /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FOCUS/MDD_TWAS_db/MDD_TWAS.db --chr ${chr} --p-threshold 5e-6 --plot --out /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD.FOCUS.MDD_TWAS_db.chr${chr}
done

Process the FOCUS results

library(data.table)

fusion <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues_TWSig_CLEAN.txt")

focus.files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/', pattern=glob2rx("MDD.FOCUS.MDD_TWAS_db.chr*.focus.tsv"))
length(focus.files)
focus<-NULL
for(i in focus.files){
focus<-rbind(focus,fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/',i)))
}

# I noticed a bug in the output where features that should be in the 90% credible set are not
focus_bug<-NULL
for(i in unique(focus$region)){
    focus_temp<-focus[focus$region == i,]
    if(sum(focus_temp$in_cred_set) == 0 & max(focus_temp$pip) != focus_temp$pip[focus_temp$ens_gene_id == 'NULL.MODEL']){
    print(head(focus_temp))
        focus_bug<-rbind(focus_bug, focus_temp)
    }
}

# This shows several features should be in the credible set
focus$in_cred_set[focus$ens_gene_id == 'DENND1B' & focus$tissue == 'cmc.brain.rnaseq' & focus$region == '1:197311514-1:199239815'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'OLFM4' & focus$tissue == 'cmc.brain.rnaseq' & focus$region == '13:53339645-13:54682393'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'ENSG00000229267' & focus$tissue == 'pec_twas_weights' & focus$region == '2:214014511-2:215573795'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'COQ3' & focus$tissue == 'cmc.brain.rnaseq_splicing' & focus$region == '6:97842747-6:100629728'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'OSBPL3' & focus$tissue == 'pituitary' & focus$region == '7:23471523-7:25077097'] <- 1
focus$in_cred_set[focus$ens_gene_id == 'PXDNL' & focus$tissue == 'cmc.brain.rnaseq' & focus$region == '8:50082470-8:53302930'] <- 1

# Update tissue for psychencode features
focus_psychencode<-focus[focus$tissue == 'pec_twas_weights',]
focus_fusion<-focus[focus$tissue != 'pec_twas_weights',]
focus_psychencode$tissue<-'psychencode'

# Update PsychENCODE gene IDs from ensembl to gene names
library(biomaRt)
ensembl = useEnsembl(biomart="ensembl", dataset="hsapiens_gene_ensembl", GRCh=37)
listAttributes(ensembl)
Genes<-getBM(attributes=c('ensembl_gene_id','external_gene_name'), mart = ensembl)

focus_psychencode<-merge(focus_psychencode, Genes, by.x='mol_name', by.y='ensembl_gene_id')
focus_psychencode$mol_name<-focus_psychencode$external_gene_name
focus_psychencode$external_gene_name<-NULL
focus_psychencode<-focus_psychencode[,names(focus),with=F]
focus<-rbind(focus_fusion,focus_psychencode)

fusion$tissue<-tolower(fusion$PANEL)
fusion_focus<-merge(fusion, focus[,c('mol_name','tissue','twas_z','pip','in_cred_set','region'),with=F], by.x=c('tissue','ID'), by.y=c('tissue','mol_name'), all.x=T)
fusion_focus<-fusion_focus[,c('CHR','P0','P1','PANEL_clean_short','ID','TWAS.Z','TWAS.P','twas_z','in_cred_set','pip','region'),with=F]
names(fusion_focus)<-c('CHR','P0','P1','SNP-weight Set','ID','TWAS.Z','TWAS.P','FOCUS_twas_z','FOCUS_in_cred_set','FOCUS_pip','FOCUS_region')
fusion_focus<-fusion_focus[order(fusion_focus$CHR, fusion_focus$P0),]
fusion_focus$Location<-paste0('chr',fusion_focus$CHR,':',fusion_focus$P0,'-',fusion_focus$P1)   
fusion_focus<-fusion_focus[,c('Location','SNP-weight Set','ID','TWAS.Z','TWAS.P','FOCUS_twas_z','FOCUS_in_cred_set','FOCUS_pip','FOCUS_region'),with=F]

write.csv(fusion_focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_sig_FOCUS_results.csv', row.names=F, quote=F)
write.csv(focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.csv', row.names=F, quote=F)

Combine FOCUS results with novelty table

library(data.table)

focus<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.csv')
fusion<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/Conditional/MDD_TWAS_Conditional_table_novelty.csv')

fusion$tissue<-tolower(fusion$PANEL)
fusion_focus<-merge(fusion, focus[,c('mol_name','tissue','twas_z','pip','in_cred_set','region'),with=F], by.x=c('tissue','ID'), by.y=c('tissue','mol_name'), all.x=T)

fusion_focus<-fusion_focus[,c('WGT','CHR','P0','P1','PANEL_clean','ID','TWAS.Z','TWAS.P','Novel','Colocalised','in_cred_set','pip','region'),with=F]
names(fusion_focus)<-c('WGT','CHR','P0','P1','SNP-weight Set','ID','TWAS.Z','TWAS.P','Novel','Colocalised','FOCUS_in_cred_set','FOCUS_pip','FOCUS_region')
fusion_focus<-fusion_focus[order(fusion_focus$CHR, fusion_focus$P0),]
fusion_focus$Location<-paste0('chr',fusion_focus$CHR,':',fusion_focus$P0,'-',fusion_focus$P1)   

# Remove the MHC region
fusion_focus_noMHC<-fusion_focus[!(fusion_focus$CHR == 6 & fusion_focus$P1 > 26e6 & fusion_focus$P0 < 34e6),]

# Subset those which are high confidence
fusion_focus_highConf<-fusion_focus_noMHC[fusion_focus_noMHC$Colocalised == T & fusion_focus_noMHC$FOCUS_pip > 0.5 & fusion_focus_noMHC$TWAS.P < 3.685926e-08,]

# One high confidence gene is novel (TMEM106B)

# Subset transcriptom-wide significant that colocalised and pip > 0.5
fusion_focus_TWsig<-fusion_focus_noMHC[fusion_focus_noMHC$TWAS.P < 1.368572e-06,]

sum(duplicated(fusion_focus_TWsig$WGT))
fusion_focus_TWsig[duplicated(fusion_focus_TWsig$WGT),]

# Subset transcriptom-wide significant that colocalised and pip > 0.5
fusion_focus_TWsig<-fusion_focus_noMHC[fusion_focus_noMHC$Colocalised == T & fusion_focus_noMHC$FOCUS_pip > 0.5 & fusion_focus_noMHC$TWAS.P < 1.368572e-06,]

write.csv(fusion_focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_sig_FOCUS_results.csv', row.names=F, quote=F)
write.csv(focus,'/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/FOCUS/MDD_TWAS_FOCUS_results.csv', row.names=F, quote=F)

Show FOCUS results

Show FOCUS and other combined results


3.8 TWAS-GSEA

Combine the predicted expression files for FUSION and PsychENCODE

library(data.table)

FUSION<-fread(cmd='zcat /scratch/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Predicted_expression/FUSION_1KG/FUSION_1KG_Expr_AllSets.csv.gz')
PsychENCODE<-fread(cmd='zcat /scratch/groups/biomarkers-brc-mh/TWAS_resource/PsychEncode/Predicted_expression/FeaturePredictions.csv.gz')

both<-merge(FUSION, PsychENCODE, by=c('FID','IID'))

fwrite(both, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv', row.names=F, quote=F)
gzip /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv

TWAS-GSEA: All tissues

# Using TWAS from all PANELs, removing duplicate genes.
sbatch -p brc,shared --mem=60G -n 3 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/candidate.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --n_cores 3 \
  --self_contained F \
  --min_r2 0.05 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Candidate_Wray

# hypothesis-free analysis
sbatch -p brc,shared --mem=60G -n 3 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/hypofree.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --min_r2 0.05 \
  --n_cores 3 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Hypo_free

# brainspan
sbatch -p brc,shared --mem=60G -n 3 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --prop_file /mnt/lustre/users/k1806347/Data/Gene_properties/Gusev_DPFC_BRAINSPAN/DFC_RIN_CLEANED.DE_ZScores.symbol.txt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --min_r2 0.05 \
  --linear_p_thresh 1 \
  --n_cores 3 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_brainspan

TWAS-GSEA: Tissue groups

library(data.table)
res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt')
Brain_res<-res[grepl('Brain|BRAIN|PsychENCODE', res$PANEL),]
HPA_res<-res[grepl('Adrenal|Pituitary|Hypothalamus', res$PANEL),]
HPT_res<-res[grepl('Thyroid|Pituitary|Hypothalamus', res$PANEL),]
BLOOD_res<-res[grepl('BLOOD', res$PANEL),]

write.table(Brain_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_BRAIN.GW', row.names=F, col.names=T, quote=F)
write.table(HPA_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_HPA.GW', row.names=F, col.names=T, quote=F)
write.table(HPT_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_HPT.GW', row.names=F, col.names=T, quote=F)
write.table(BLOOD_res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_BLOOD.GW', row.names=F, col.names=T, quote=F)
# Candidate
for set in $(echo BRAIN HPA HPT BLOOD);do
sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_${set}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/candidate.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --n_cores 1 \
  --self_contained F \
  --min_r2 0.05 \
  --competitive T \
  --linear_p_thresh 1 \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/${set}_GSEA_Candidate_Wray
done

# Hypo-free
for set in $(echo BRAIN HPA HPT BLOOD);do
sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_${set}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/hypofree.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --n_cores 1 \
  --self_contained F \
  --min_r2 0.05 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/${set}_GSEA_Hypo_free
done

# brainspan
for set in $(echo BRAIN HPA HPT BLOOD);do
sbatch -p brc,shared --mem=20G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanelSet/MDD_TWAS_${set}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --prop_file /mnt/lustre/users/k1806347/Data/Gene_properties/Gusev_DPFC_BRAINSPAN/DFC_RIN_CLEANED.DE_ZScores.symbol.txt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --min_r2 0.05 \
  --n_cores 3 \
  --linear_p_thresh 1 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/${set}_brainspan

done

TWAS-GSEA: Tissue-specific

library(data.table)
res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/MDD_TWAS_AllTissues.txt')

for(i in unique(res$PANEL)){
  write.table(res[res$PANEL == i,], paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_',i,'.GW'), row.names=F, col.names=T, quote=F)
}
for tissue in $(cat /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt); do

sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_${tissue}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/candidate.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --n_cores 1 \
  --self_contained F \
  --linear_p_thresh 1 \
  --competitive T \
  --min_r2 0.05 \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/${tissue}_GSEA_Candidate_Wray

done

for tissue in $(cat /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt); do

sbatch -p brc,shared --mem=20G /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_${tissue}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --gmt_file /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/hypofree.gmt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --competitive T \
  --min_r2 0.05 \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/${tissue}_GSEA_Hypo_free

done

# brainspan
for tissue in $(cat /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWASweights_list_withPsychENCODE.txt); do

sbatch -p brc,shared --mem=20G -n 1 /users/k1806347/brc_scratch/Software/Rscript.sh /mnt/lustre/groups/biomarkers-brc-mh/TWAS_resource/FUSION/Scripts/Git/opain/TWAS-GSEA/TWAS-GSEA.V1.2.R \
  --twas_results /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS/ByPanel/MDD_TWAS_${tissue}.GW \
  --pos /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/MDD_TWAS.pos \
  --prop_file /mnt/lustre/users/k1806347/Data/Gene_properties/Gusev_DPFC_BRAINSPAN/DFC_RIN_CLEANED.DE_ZScores.symbol.txt \
  --qqplot F \
  --use_alt_id ID \
  --expression_ref /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/FUSION_PsychENCODE_FeaturePredictions.csv.gz \
  --self_contained F \
  --min_r2 0.05 \
  --linear_p_thresh 1 \
  --n_cores 3 \
  --competitive T \
  --covar GeneLength,NSNP \
  --output /users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/${tissue}_brainspan

done

# Note. The individual tissue analyses return more significant findings. Instead of aggregating tissues, perhaps meta-analysis of per tissue results would be more effective.

Tabulate the results

library(data.table)

#####
# AllTissue results
#####
##
# Candidate
##

res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Candidate_Wray.competitive.txt')

res<-res[res$P.CORR < 0.05,]
res<-data.frame( GeneSet=gsub('\\.getlink.*','',res$GeneSet),
                            PMID=gsub('.*\\.','',res$GeneSet),
                            res[,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])

write.csv(res, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/AllTissue_GSEA_Candidate_Wray_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)

##
# Hpothesis free
##

res<-fread('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_GSEA_Hypo_free.competitive.txt')

res<-res[res$P.CORR < 0.05,]
res<-data.frame( GeneSet=gsub('\\.getlink.*','',res$GeneSet),
                            PMID=gsub('.*\\.','',res$GeneSet),
                            res[,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])

####
# Tissue-set analysis
####

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet', pattern='competitive.txt')

res_files<-res_files[grepl('Candidate', res_files)]

tissue_cand_res<-list()
for(i in res_files){
tissue_cand_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/',i))
tissue_cand_res[[i]]<-tissue_cand_res[[i]][tissue_cand_res[[i]]$P.CORR < 0.05,]
tissue_cand_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_cand_res[[i]]$GeneSet),
                            PMID=gsub('.*\\.','',tissue_cand_res[[i]]$GeneSet),
                            tissue_cand_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}

tissue_cand_res_all<-do.call(rbind, tissue_cand_res)
tissue_cand_res_all<-tissue_cand_res_all[order(tissue_cand_res_all$P.CORR),]
tissue_cand_res_all$Tissue<-gsub('_GSEA_Candidate.*','',row.names(tissue_cand_res_all))
row.names(tissue_cand_res_all)<-NULL

write.csv(tissue_cand_res_all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/TissueSet_GSEA_Candidate_Wray_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet', pattern='competitive.txt')

res_files<-res_files[grepl('Hypo_free', res_files)]

tissue_hypo_res<-list()
for(i in res_files){
tissue_hypo_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/',i))
tissue_hypo_res[[i]]<-tissue_hypo_res[[i]][tissue_hypo_res[[i]]$P.CORR < 0.05,]
tissue_hypo_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_hypo_res[[i]]$GeneSet),
                            tissue_hypo_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}

tissue_hypo_res_all<-do.call(rbind, tissue_hypo_res)
tissue_hypo_res_all<-tissue_hypo_res_all[order(tissue_hypo_res_all$P.CORR),]
tissue_hypo_res_all$Tissue<-gsub('_GSEA_Hypo.*','',row.names(tissue_hypo_res_all))
row.names(tissue_hypo_res_all)<-NULL

####
# Tissue specific analyses
####

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel', pattern='competitive.txt')

res_files<-res_files[grepl('Candidate', res_files)]

tissue_cand_res<-list()
for(i in res_files){
tissue_cand_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/',i))
tissue_cand_res[[i]]<-tissue_cand_res[[i]][tissue_cand_res[[i]]$P.CORR < 0.05,]
tissue_cand_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_cand_res[[i]]$GeneSet),
                            PMID=gsub('.*\\.','',tissue_cand_res[[i]]$GeneSet),
                            tissue_cand_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}

tissue_cand_res_all<-do.call(rbind, tissue_cand_res)
tissue_cand_res_all<-tissue_cand_res_all[order(tissue_cand_res_all$P.CORR),]
tissue_cand_res_all$Tissue<-gsub('_GSEA_Candidate.*','',row.names(tissue_cand_res_all))
row.names(tissue_cand_res_all)<-NULL

write.csv(tissue_cand_res_all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/TissueSpecific_GSEA_Candidate_Wray_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel', pattern='competitive.txt')

res_files<-res_files[grepl('Hypo_free', res_files)]

tissue_hypo_res<-list()
for(i in res_files){
tissue_hypo_res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/',i))
tissue_hypo_res[[i]]<-tissue_hypo_res[[i]][tissue_hypo_res[[i]]$P.CORR < 0.05,]
tissue_hypo_res[[i]]<-data.frame( GeneSet=gsub('\\.getlink.*','',tissue_hypo_res[[i]]$GeneSet),
                            tissue_hypo_res[[i]][,c('Estimate','SE','T','N_Mem_Avail','N_Mem','P','P.CORR'),with=F])
}

tissue_hypo_res_all<-do.call(rbind, tissue_hypo_res)
tissue_hypo_res_all<-tissue_hypo_res_all[order(tissue_hypo_res_all$P.CORR),]
tissue_hypo_res_all$Tissue<-gsub('_GSEA_Hypo.*','',row.names(tissue_hypo_res_all))
row.names(tissue_hypo_res_all)<-NULL

write.csv(tissue_hypo_res_all, '/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/TissueSpecific_GSEA_Hypo_free_latest.competitive.Significant.csv', col.names=T, row.names=F, quote=F)

############
# Table and make figures for brainspan enrichment analysis
############

##
# All tissue
##

library(ggplot2)
library(stringr)
library(cowplot)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA', pattern='competitive.txt')

res_files<-res_files[grepl('brainspan', res_files)]

res<-list()
res_plot<-list()
for(i in res_files){
res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/',i))

res[[i]]$P.CORR<-NULL
res[[i]]$P<-2*pnorm(-abs(res[[i]]$T))

res[[i]]$Stage<-gsub('^X','',res[[i]]$GeneSet)
res[[i]]$Stage<-gsub('_pcw',' pcw',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_years',' yrs',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_mos',' mos',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_',' - ',res[[i]]$Stage)

res[[i]]<-cbind(res[[i]],data.frame(str_split_fixed(res[[i]]$Stage, " ", 2)))
res[[i]]$X1<-as.numeric(as.character(res[[i]]$X1))
res[[i]]$X2<-factor(res[[i]]$X2, levels=c('pcw','mos','yrs'))
res[[i]]<-res[[i]][order(res[[i]]$X2,res[[i]]$X1),]
res[[i]]$Stage<-factor(res[[i]]$Stage, levels=res[[i]]$Stage)
res[[i]]$Z<-sign(res[[i]]$Estimate)*(qnorm(1-(res[[i]]$P/2)))
res[[i]]$Group<-"None"
res[[i]]$Group[res[[i]]$Estimate > 0 & res[[i]]$P < 0.05]<-'Positive'
res[[i]]$Group[res[[i]]$Estimate < 0 & res[[i]]$P < 0.05]<-'Negative'

res_plot[[i]]<-ggplot(res[[i]], aes(x=Stage, y=Z, fill=Group)) +
  geom_bar(stat="identity", position=position_dodge()) +
  theme_half_open() +
  background_grid() +
  scale_fill_manual(values=c(Positive = "#FF3333", Negative = "#3399FF", None="#999999")) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        legend.position = 'none') +
  ggtitle('All Tissues') +
  geom_hline(yintercept=qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed") +
  geom_hline(yintercept=-qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=-qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed")
}

png('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/MDD_TWAS_AllTissue_BrainSpan.png', units='px', res=300, width=1500, height=1000)
plot_grid(plotlist=res_plot, ncol=1)
dev.off()

##
# Tissue set
##

library(ggplot2)
library(stringr)
library(cowplot)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet', pattern='competitive.txt')

res_files<-res_files[grepl('brainspan', res_files)]

res<-list()
res_plot<-list()
for(i in res_files){
res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/',i))

res[[i]]$P.CORR<-NULL
res[[i]]$P<-2*pnorm(-abs(res[[i]]$T))

res[[i]]$Stage<-gsub('^X','',res[[i]]$GeneSet)
res[[i]]$Stage<-gsub('_pcw',' pcw',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_years',' yrs',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_mos',' mos',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_',' - ',res[[i]]$Stage)

res[[i]]<-cbind(res[[i]],data.frame(str_split_fixed(res[[i]]$Stage, " ", 2)))
res[[i]]$X1<-as.numeric(as.character(res[[i]]$X1))
res[[i]]$X2<-factor(res[[i]]$X2, levels=c('pcw','mos','yrs'))
res[[i]]<-res[[i]][order(res[[i]]$X2,res[[i]]$X1),]
res[[i]]$Stage<-factor(res[[i]]$Stage, levels=res[[i]]$Stage)
res[[i]]$Z<-sign(res[[i]]$Estimate)*(qnorm(1-(res[[i]]$P/2)))
res[[i]]$Group<-"None"
res[[i]]$Group[res[[i]]$Estimate > 0 & res[[i]]$P < 0.05]<-'Positive'
res[[i]]$Group[res[[i]]$Estimate < 0 & res[[i]]$P < 0.05]<-'Negative'

res_plot[[i]]<-ggplot(res[[i]], aes(x=Stage, y=Z, fill=Group)) +
  geom_bar(stat="identity", position=position_dodge()) +
  theme_half_open() +
  background_grid() +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        legend.position = 'none') +
  ggtitle(gsub('_brainspan.competitive.txt','',i)) +
  geom_hline(yintercept=qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed") +
  scale_fill_manual(values=c(Positive = "#FF3333", Negative = "#3399FF", None="#999999")) +
  geom_hline(yintercept=-qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=-qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed")
}

png('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanelSet/MDD_TWAS_ByPanelSet_BrainSpan.png', units='px', res=300, width=2500, height=1500)
plot_grid(plotlist=res_plot, ncol=2)
dev.off()

##
# Tissue-specific
##

library(ggplot2)
library(stringr)
library(cowplot)

res_files<-list.files(path='/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel', pattern='competitive.txt')

res_files<-res_files[grepl('brainspan', res_files)]

res<-list()
res_plot<-list()
for(i in res_files){
res[[i]]<-fread(paste0('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/',i))

res[[i]]$P.CORR<-NULL
res[[i]]$P<-2*pnorm(-abs(res[[i]]$T))

res[[i]]$Stage<-gsub('^X','',res[[i]]$GeneSet)
res[[i]]$Stage<-gsub('_pcw',' pcw',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_years',' yrs',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_mos',' mos',res[[i]]$Stage)
res[[i]]$Stage<-gsub('_',' - ',res[[i]]$Stage)

res[[i]]<-cbind(res[[i]],data.frame(str_split_fixed(res[[i]]$Stage, " ", 2)))
res[[i]]$X1<-as.numeric(as.character(res[[i]]$X1))
res[[i]]$X2<-factor(res[[i]]$X2, levels=c('pcw','mos','yrs'))
res[[i]]<-res[[i]][order(res[[i]]$X2,res[[i]]$X1),]
res[[i]]$Stage<-factor(res[[i]]$Stage, levels=res[[i]]$Stage)
res[[i]]$Z<-sign(res[[i]]$Estimate)*(qnorm(1-(res[[i]]$P/2)))
res[[i]]$Group<-"None"
res[[i]]$Group[res[[i]]$Estimate > 0 & res[[i]]$P < 0.05]<-'Positive'
res[[i]]$Group[res[[i]]$Estimate < 0 & res[[i]]$P < 0.05]<-'Negative'

res_plot[[i]]<-ggplot(res[[i]], aes(x=Stage, y=Z, fill=Group)) +
  geom_bar(stat="identity", position=position_dodge()) +
  theme_half_open() +
  background_grid() +
  scale_fill_manual(values=c(Positive = "#FF3333", Negative = "#3399FF", None="#999999")) +
  theme(axis.text.x = element_text(angle = 45, vjust = 1, hjust=1), 
        legend.position = 'none') +
  ggtitle(gsub('_brainspan.competitive.txt','',i)) +
  geom_hline(yintercept=qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed") +
  geom_hline(yintercept=-qnorm(1-(0.05/2)),linetype="dotted") +
  geom_hline(yintercept=-qnorm(1-(((0.05/dim(res[[i]])[1])/2))),linetype="dashed")
}

png('/users/k1806347/brc_scratch/Analyses/Lorenza/Clean/TWAS-GSEA/ByPanel/MDD_TWAS_ByPanel_BrainSpan.png', units='px', res=300, width=3000, height=7000)
plot_grid(plotlist=res_plot, ncol=2)
dev.off()

Show candidate TWAS-GSEA results

All tissue results
GeneSet PMID Estimate SE T N_Mem_Avail N_Mem P P.CORR
RBFOX2 24613350 0.08092875 0.02482267 3.260276 2445 3031 0.0005565194 0.03951288
Tissue-Set results
GeneSet PMID Estimate SE T N_Mem_Avail N_Mem P P.CORR Tissue
RBFOX2 24613350 0.10697919 0.02587590 4.134318 2373 3031 1.780048e-05 0.001263834 BRAIN
SCZ.COMPOSITE 24463508 0.11324126 0.03114801 3.635586 1343 1787 1.386748e-04 0.004922957 BRAIN
RBFOX1.RBFOX3 24613350 0.08366670 0.02424599 3.450744 2627 3400 2.795220e-04 0.006615355 BRAIN
FMRP 21784246 0.12461728 0.03715019 3.354419 937 1240 3.976596e-04 0.007058457 BRAIN
POTENTIALLY.SYNAPTIC.ALL 27694994 0.06031635 0.01988561 3.033166 4384 5736 1.210011e-03 0.017182151 BRAIN
PGC.BP.P10.4 21926972 0.18130846 0.06366349 2.847919 324 629 2.200306e-03 0.026036954 BRAIN
NEURONAL.PSD 23071613 0.08920852 0.03345738 2.666333 1131 1444 3.834183e-03 0.038889571 BRAIN
Tissue-Specific results
GeneSet PMID Estimate SE T N_Mem_Avail N_Mem P P.CORR Tissue
MIR.137 24463508 0.3550824 0.09912228 3.582266 130 421 0.0001703133 0.01021880 CMC.BRAIN.RNASEQ
SCZ.DENOVO.NONSYN 24463508 0.4039377 0.11510465 3.509308 83 604 0.0002246369 0.01168112 Pituitary
SCZ.COMPOSITE 24463508 0.2323825 0.07087671 3.278687 233 1787 0.0005214569 0.01355788 Pituitary
SCZ.COMPOSITE 24463508 0.2677617 0.08385283 3.193234 188 1787 0.0007034446 0.03235845 Brain_Caudate_basal_ganglia
CONSTRAINED 25086666 0.2667025 0.08985060 2.968289 157 1003 0.0014973135 0.03436180 CMC.BRAIN.RNASEQ
RBFOX1.RBFOX3 24613350 0.1219388 0.04167769 2.925758 938 3400 0.0017180898 0.03436180 CMC.BRAIN.RNASEQ
PGC.SCZ.P10.4 24463508 0.2749434 0.10082558 2.726921 155 442 0.0031964205 0.04794631 CMC.BRAIN.RNASEQ

Show hypothesis-free TWAS-GSEA results

Tissue-Specific results
GeneSet Estimate SE T N_Mem_Avail N_Mem P P.CORR Tissue
GO.MACROMOLECULAR.COMPLEX.BINDING 0.4625439 0.09122296 5.070477 152 1365 1.984100e-07 0.000598603 Brain_Caudate_basal_ganglia
GO.MICROTUBULE.BINDING 1.0368947 0.22172666 4.676455 25 191 1.459384e-06 0.002201480 Brain_Caudate_basal_ganglia
GO.ALCOHOL.BINDING 1.7881848 0.38352213 4.662534 8 99 1.561701e-06 0.005161421 Pituitary
GO.CHROMATIN.BINDING 0.7897970 0.18867269 4.186070 35 422 1.419132e-05 0.014077837 Brain_Caudate_basal_ganglia
GO.PROTEIN.COMPLEX.BINDING 0.4429150 0.10741456 4.123417 110 913 1.866468e-05 0.014077837 Brain_Caudate_basal_ganglia
GO.LIGAND.DEPENDENT.NUCLEAR.RECEPTOR.BINDING 1.9756846 0.49658009 3.978582 5 22 3.466376e-05 0.020916114 Brain_Caudate_basal_ganglia
GO.REGULATION.OF.INTRINSIC.APOPTOTIC.SIGNALING.PATHWAY 1.7918397 0.43272770 4.140802 6 138 1.730469e-05 0.032982733 Brain_Amygdala

Show developmental stage enrichment plots

MDD TWAS: All Tissues BrainSpan

MDD TWAS: All Tissues BrainSpan


MDD TWAS: Tissue-sets BrainSpan

MDD TWAS: Tissue-sets BrainSpan


MDD TWAS: Tissue-specific BrainSpan ***


4 Comparison with previous TWAS literature

I need Lorenza to send me the results files whe prepared previously for this comparison. Alternatively, Lorenza could run this section. The below code has not been run or edited to work with new files yet.

4.1 Wray et al. (2018) and Gaspar et al. (2019)

Show code

########################################
# Comparing our results to previous TWASs
###############################################
# our TWAS, Wray et al TWAS, Gaspar et al. TWAS

rm(list=ls())
###
# Load data
###
library(data.table)
#these are all files with significant features only. NB the Gaspar file includes findings from all snp weights from any 
#gene sign. in at least one tissue
our_hits <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Output/raw_findings/AllTissues_CLEAN.txt")
our_hits_sign <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Output/raw_findings/signtest.AllTissues_CLEAN.txt")
Gaspar_hits <- fread("C:/Users/loryd/Desktop/MSc dissertation/mock data/Gaspar et al. hits.txt")
Wray_hits <- fread("C:/Users/loryd/Desktop/MSc dissertation/mock data/Wray et al. hits.csv")

###
# Structure df so that they are similar
###

#1. GASPAR (NB this includes info on TWAS.Z values not p values!!!)
#melt the gaspar hits df so that it presents one column with all SNP-weigths and one with the values
Gaspar_hits2 <- melt(Gaspar_hits, id = c("target", "CHROMOSOME"))
head(Gaspar_hits2)

#rename cols
Gaspar_hits2$PANEL <- Gaspar_hits2$variable
Gaspar_hits2$TWAS.Z <- Gaspar_hits2$value

Gaspar_hits2$variable <- NULL
Gaspar_hits2$value <- NULL

#2. Wray et al 

#keep panel, ID, and TWAS.P & TWAS.Z info only 
library(tidyverse)
Wray_hits <- as_tibble(Wray_hits)
Wray_hits
Wray_hits_filt <- Wray_hits %>% select("Gene", "CHR", "TWAS.Z", "TWAS.P")


#ALL
#rename columns so they all correspond 
Gaspar_hits2$ID <- Gaspar_hits2$target
Gaspar_hits2$CHR <- Gaspar_hits2$CHROMOSOME
str(Gaspar_hits2)

Gaspar_hits2$target <- NULL
Gaspar_hits2$CHROMOSOME <- NULL

Wray_hits_filt$ID <- Wray_hits_filt$Gene
Wray_hits_filt$Gene <- NULL


#filter our findings for the  cols needed only 
our_hits <- as_tibble(our_hits)
colnames(our_hits)
our_hits_filt <- our_hits %>% select("PANEL", "PANEL_clean_short", "ID", "CHR", "P0", "P1", "TWAS.Z", "TWAS.P")

###
# Fix SNP-weights 
###
#No need for this
#Wray_hits_filt$PANEL_Wray <- "CMC DLPFC"
#our_hits_filt$PANEL_DallAglio <- our_hits_filt$PANEL_clean_short
#Gaspar_hits2$PANEL_Gaspar <- Gaspar_hits2$PANEL_Gaspar

###
# Filter our findings for any gene significant in either study 
###

sign_genes_our <- our_hits_sign$ID
sign_genes_Wray <- Wray_hits_filt$ID
sign_genes_Gaspar <- Gaspar_hits2$ID

Gaspar <- unique(sign_genes_Gaspar) #25 unique genes
Wray <- unique(sign_genes_Wray) #17 unique genes
Us <- unique(sign_genes_our) #91 unique genes 

#create a general variable with all sign. genes in any paper
All <- c(Gaspar, Wray, Us)
All #133 features

#keep only unique gene IDs
All_unique <- unique(All)
All_unique #106 genes

###
#Keep only genes sign, in either of the three studies
###

#filter by genes which are in the vector containing sign. features in any of the studies
our_hits_final <- our_hits_filt[(our_hits_filt$ID %in% All_unique), ]  #629 observations

#keep only unique genes in our study (those with the greatest absolute z-score)
our_hits_final<-our_hits_final[!is.na(our_hits_final$TWAS.Z),]
our_hits_final2 <- our_hits_final[order(abs(our_hits_final$TWAS.Z), decreasing = T), ]
library(dplyr)
our_hits_final3  <- our_hits_final2 %>% distinct(ID, .keep_all = T) #102 obs.

our_hits_correct <- our_hits_final3

#keep only unique genes in the Gaspar et al study 
Gaspar_hits2b<-Gaspar_hits2[!is.na(Gaspar_hits2$TWAS.Z),]
Gaspar_hits3 <- Gaspar_hits2b[order(abs(Gaspar_hits2b$TWAS.Z), decreasing = T), ]
library(dplyr)
Gaspar_hits4  <- Gaspar_hits3 %>% distinct(ID, .keep_all = T) #25 gene IDs, as expected 

Gaspar_correct <- Gaspar_hits4

#NB no need to keep only unique genes for the Wray et al TWAS as that is tested in one tissue only
#exclude missings in the Wray et al 
Wray_hits_correct <- Wray_hits_filt[!is.na(Wray_hits_filt$TWAS.Z), ] 
Wray_hits_correct

###
# Change the names of variables which have the same col name but distinct values across the three df
###

Gaspar_correct$Gaspar_TWAS.Z <- Gaspar_correct$TWAS.Z
Gaspar_correct$TWAS.Z <- NULL

Wray_hits_correct$Wray_TWAS.Z <- Wray_hits_correct$TWAS.Z
Wray_hits_correct$TWAS.Z <- NULL

Wray_hits_correct$Wray_TWAS.P <- Wray_hits_correct$TWAS.P
Wray_hits_correct$TWAS.P <- NULL

our_hits_correct$DallAglio_TWAS.Z <- our_hits_correct$TWAS.Z
our_hits_correct$DallAglio_TWAS.P <- our_hits_correct$TWAS.P

our_hits_correct$TWAS.Z <- NULL
our_hits_correct$TWAS.P <- NULL
our_hits_correct$PANEL <- NULL
our_hits_correct$PANEL_clean_short <- NULL


Wray_hits_correct$CHR <- NULL
Wray_hits_correct$Wray_TWAS.P <- NULL
Wray_hits_correct$TWAS.P <- NULL


Gaspar_correct$CHR <- NULL
Gaspar_correct$PANEL <- NULL

###
#join your hits with the hits from the wray et al. paper and Gaspar et al paper
###
#since the three tibbles do not have the same nrow, we cannot use merge. But we can use full_join

Gaspar_correct <- as_tibble(Gaspar_correct) #transform all df into tibbles to use full join. the other two
##df are already tibbles. this was the only one left to convert

#join hits bw Gaspar and our study
table_correct<- full_join(our_hits_correct, Gaspar_correct) #dim 106, 7

#merge the newly created table with the wray et al findings too
table_correct2 <- full_join(table_correct, Wray_hits_correct)  #dim 106, 8 


#check that no gene is repeated and that z-scores are present for the three studies (should be high z-scores)
table_correct2 <- table_correct2[order(table_correct2$ID), ]
table_correct2 #everything looks fine

#delete the Dall'Aglio TWAS.P col 
table_correct2$DallAglio_TWAS.P <- NULL

#add a column saying whether the gene was transcriptome-wide sign. in our study 
table_correct2$Transcriptome_wide_sign_DallAglio <- ifelse(table_correct2$DallAglio_TWAS.Z > 4.74 | table_correct2$DallAglio_TWAS.Z < -4.74, "Sign.", "Not Sign.")

###
# Clean up and save
###

my_data <- as_data_frame(table_correct2)
colnames(my_data)

col_order <- c("ID", "Gaspar_TWAS.Z", "Wray_TWAS.Z", "DallAglio_TWAS.Z", "Transcriptome_wide_sign_DallAglio")
my_data2 <- my_data[, col_order]
my_data2


#save as csv file (each df col is treated as independent col, +excel format)
write.csv(my_data2, '/users/k1806347/brc_scratch/Analyses/Lorenza/Output/table/comparison_previousTWASs.csv', row.names=F)
my_data2

Findings When comparing the Wray et al. (2018) results from the CMC DLPFC with our findings, we can observe that we obtained identical results in terms of TWAS z-values. This is true also for two genes which do not show concordance in effect size: SLC25A17 and DENND1B. This is due to us findings stronger associations in those genes in panels which were not tested by the Wray et al study (i.e. the nucleus accumbens and the CMC DLPFC splicing). Generally, Wray et al. (2018) identified a greater number of associations, but this was due to the lower multiple testing burden they presented as opposed to our study which tested 20 SNP-weight sets instead of one.

When comparing the Gaspar et al. (2019) results with our findings, it can be noted that results were generally very similar. Inconsistencies were present in four genes (DENND1B, KLC1, ZMYND8, ZNF165), where different directions of effect were identified. This is due to (i) the association being the strongest in our study within the CMC DLPFC splicing panel (where inconsistent direction of effects are generally present), (ii) our finding being from a SNP-weight panel not tested in the Gaspar et al. study (e.g. KLC1 z-score = -4.7 for the thyroid and ZNF165 z-score = 4.18 for the thyroid tissue), (iii) the Gaspar et al. study testing SNP-weight panels we did not test (e.g. for ZMYND8, a high z-score was found by them in the DGN whole blood panel). Interestingly, four associations within genes which were not tested in our study due to their low heritability in the tested SNP-weights were instead identified by the Gaspar study, namely BTN1A1, HIST1H2AK, TMEM33, TMX2.These were the strongest and significant in the DGN blood weight set (for BTN1A1, HIST1H2AK and TMX2), and in the GTEx cerebellum SNPweight set for TMEM33. The latter is of high interest since the same SNP-weight was tested in our study, but it could not surpass the heritability threshold for feature selection.

5 Comparison with the observed gene expression TWAS of depression

5.1 Our finding compared to those from Jansen et al. (2016)

Show code

##################
# Comparing our findings to the findings from Jansen et al. (TWAS of observed gene expression)
##################

###
# Load data
###

rm(list=ls())

library(data.table)
library(dplyr)
our_hits <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Output/raw_findings/signtest.AllTissues_CLEAN.txt")
Jansen_alloutput <- fread("C:/Users/loryd/Desktop/MSc dissertation/mock data/Jansen et al. hits.csv")
our_alloutput <- fread("/users/k1806347/brc_scratch/Analyses/Lorenza/Output/raw_findings/AllTissues_CLEAN.txt")

#filter for significant features only in the Jansen paper
colnames(Jansen_alloutput)
Jansen_sign <- filter(Jansen_alloutput, Jansen_alloutput$`FDR control vs current`< 0.1) #this is for the Jansenvsour study comparison

#keep only needed columns in both df 
library(tidyverse)
our_hits <- as_tibble(our_hits)
our_hits
our_hits_filt <- our_hits %>% select("ID", "CHR", "P0", "P1", "TWAS.Z", "TWAS.P", "PANEL_clean_short")


###
#Comparing our results vs Jansen et al's 
###
our_hits[order(our_hits$ID), ]
Jansen_alloutput[order(Jansen_alloutput$Gene), ]

table_ourvsJansen2 <- merge(our_hits, Jansen_alloutput, by.x = "ID", by.y = "Gene")

validated <- table_ourvsJansen2[(table_ourvsJansen2$`P control vs current`< 0.05), ]
#42 features which were significant in our study, were also nominally significant in their study (for either one of the three comparison types)

unique(validated$ID)
#these 42 hits corresponded to 14 unique genes
#[1] "ANKRD44"  "CKB"      "COQ3"     "DLST"     "EP300"    "FLOT1"    "OSBPL3"   "PCDHA8"   "RAB27B"  
#[10] "RERE"     "SYNE2"    "TMEM106B" "TRMT61A"  "ZSCAN16" 

nrow(validated)  #42 rows


###
#create a table comparing our findings to theirs
###

#Order cols as you like
library(tibble)
validated <- as_data_frame(validated)
colnames(validated)

#order the table by CHR and then P0
str(validated)
validated$CHR <- as.numeric(as.character(validated$CHR))
validated$P0 <- as.numeric(as.character(validated$P0))
validated <- validated[order(validated$CHR, validated$P0), ]


#fix columns
validated$Location <- paste0('chr',validated$CHR,':', validated$P0,'-',validated$P1)
validated$DallAglio_Zscore <- validated$TWAS.Z
validated$DallAglio_pvalue <- validated$TWAS.P
validated$Jansen_pvalue_controlsvscurrent <- validated$`P control vs current` 
validated$Jansen_zscore_controlsvscurrent <- validated$`B control vs current`

#set a column order
col_order <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent")

validated <- validated[, col_order]
validated

#add columns with 1) whether assoc. surpass the bonferroni threshold, 2) specifying whether the direction
# of effects is consistent across the two studies

#1) Add the column with surpassing the Bonferroni sign. or not
0.05 / 14    #Bonf. significance = nominal p value / number of unique genes
# = 0.003571429

validated$Bonf_validated <- ifelse(validated$Jansen_pvalue_controlsvscurrent < 0.003571429, "Yes", "No")

#2) add the column specifying whether direction of effects is consistent

validated$Consistent_dir_effect <- ifelse((validated$Jansen_zscore_controlsvscurrent > 0 & validated$DallAglio_Zscore > 0) | (validated$Jansen_zscore_controlsvscurrent < 0 & validated$DallAglio_Zscore <0), "Yes", "No")

sum(validated$Consistent_dir_effect == "Yes")  #17 associations present the same direction of effect
sum(validated$Consistent_dir_effect == "No") #25 associations present a different direction of effect

sum(validated$Bonf_validated == "Yes") #12 associations Bonferroni validated
print(validated$ID[validated$Bonf_validated == "Yes"]) # "RERE"     "RERE"     "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B" "TMEM106B"
#"TMEM106B" "EP300"    "EP300" 

#these 12 validated associations come from 3 unique genes = RERE, TMEM106B, EP300

#change col order again
col_order_2 <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent", "Consistent_dir_effect", "Bonf_validated")

validated <- validated[, col_order_2]
head(validated)


#save as csv file (each df col is treated as independent col, +excel format)
write.csv(validated, '/users/k1806347/brc_scratch/Analyses/Lorenza/Output/table/OurFindingsvsJansen_correct.csv', row.names = F)

validated

Findings When comparing our findings to those from Jansen et al. (2016) it can be observed that their results could validate 42 of our associations at a nominal p-value level and 12 at a Bonferroni significance level. Of these associations, 14 and 3 unique genes could be validated, respectively. The three Bonferroni validated genes include RERE, TMEM106B, EP300. However, for RERE, TMEM106B, inconsistent direction of effect was generally present. Contrarily, EP300 was consistently upregulated in both studies.

5.1.1 Jansen et al. (2016) findings compared to ours

Show code

####
# Comparing the Jansen et al results to ours 
####
#this script is the continuation of the one before

#order both df by gene ID
our_alloutput[order(our_alloutput$ID), ]
Jansen_sign[order(Jansen_sign$Gene), ]

#merge
table_Jansenvsours <- merge(our_alloutput, Jansen_sign, by.x = "ID", by.y = "Gene")

Replicated_byus <- table_Jansenvsours[(table_Jansenvsours$TWAS.P < 0.05), ]
#54 observations

unique(Replicated_byus$ID)
#pertain to 28 IDs:  [1] "AMICA1"  "ARHGEF7" "ARL4C"   "ASPH"    "CCDC116" "CD47"    "COA1"    "CPEB4"   "DDHD1"   "DENND4C" "FBXO3"   "GNPTAB" 
#[13] "GOT2"    "IL6R"    "INVS"    "KTN1"    "MBNL1"   "MEFV"    "MTSS1"   "MYH9"    "NAPG"    "NCALD"   "NUPL2"   "OSTM1"  
#[25] "PAPPA2"  "SP4"     "TMEM136" "TMEM64"

###
#FIx the table
###

#Order cols as you like
library(tibble)
Replicated_byus <- as_data_frame(Replicated_byus)
colnames(Replicated_byus)

#order the table by CHR and then P0
str(Replicated_byus)
Replicated_byus$CHR <- as.numeric(as.character(Replicated_byus$CHR))
Replicated_byus$P0 <- as.numeric(as.character(Replicated_byus$P0))
Replicated_byus$P1 <- as.numeric(as.character(Replicated_byus$P1))

Replicated_byus <- Replicated_byus[order(Replicated_byus$CHR, Replicated_byus$P0), ]


#fix columns
Replicated_byus$Location <- paste0('chr',Replicated_byus$CHR,':', Replicated_byus$P0,'-',Replicated_byus$P1)
Replicated_byus$DallAglio_Zscore <- Replicated_byus$TWAS.Z
Replicated_byus$DallAglio_pvalue <- Replicated_byus$TWAS.P
Replicated_byus$Jansen_pvalue_controlsvscurrent <- Replicated_byus$`P control vs current` 
Replicated_byus$Jansen_zscore_controlsvscurrent <- Replicated_byus$`B control vs current`

#set a column order
col_order3 <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent")

Replicated_byus <- Replicated_byus[, col_order3]
Replicated_byus

#add columns with 1) whether assoc. surpass the bonferroni threshold, 2) specifying whether the direction
# of effects is consistent across the two studies

#1) Add the column with surpassing the Bonferroni sign. or not
0.05 / 28    #Bonf. significance = nominal p value / number of unique genes
# = 0.001785714

Replicated_byus$Bonf_validated <- ifelse(Replicated_byus$DallAglio_pvalue < 0.001785714, "Yes", "No")

#2) add the column specifying whether direction of effects is consistent

Replicated_byus$Consistent_dir_effect <- ifelse((Replicated_byus$Jansen_zscore_controlsvscurrent > 0 & Replicated_byus$DallAglio_Zscore > 0) | (Replicated_byus$Jansen_zscore_controlsvscurrent < 0 & Replicated_byus$DallAglio_Zscore <0), "Yes", "No")

sum(Replicated_byus$Consistent_dir_effect == "Yes")  #26 associations present the same direction of effect
sum(Replicated_byus$Consistent_dir_effect == "No") #28 associations present a different direction of effect

sum(Replicated_byus$Bonf_validated == "Yes") #9 associations Bonferroni validated
print(Replicated_byus$ID[Replicated_byus$Bonf_validated == "Yes"]) 
# "PAPPA2" "MBNL1"  "TMEM64" "TMEM64" "TMEM64" "TMEM64" "GNPTAB" "KTN1"   "KTN1"

#these come from 5 unique genes = PAPPA2, MBNL1, TMEM64, GNPTAB, KTN1 

#change col order again
col_order_3 <- c("Location", "ID", "PANEL_clean_short", "DallAglio_Zscore", "Jansen_zscore_controlsvscurrent", "DallAglio_pvalue", "Jansen_pvalue_controlsvscurrent", "Consistent_dir_effect", "Bonf_validated")

Replicated_byus <- Replicated_byus[, col_order_3]
head(Replicated_byus)


#save as csv file 
write.csv(Replicated_byus, '/users/k1806347/brc_scratch/Analyses/Lorenza/Output/table/Jansenvsourfindings_correct.csv', row.names = F)

Replicated_byus

Findings 54 associations in the Jansen et al. (2016) study were replicated by our findings at the nominal level in any tissue, while 9 at a Bonferroni significance threshold. Of these, 28 and 5 were from unique genes, respectively. Such unique genes are PAPPA2, MBNL1, TMEM64, GNPTAB, KTN1. Of note, half of the feature associations presented the same direction of effect.


Report ended